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The above given is the architecture diagram. It describes that how the images are watermarked and uploaded in the social network sites. In which the original user will watermark the image and upload it in the social network. While the admin will access details of the images that have been stored in the server i.e. the social network sites. During the process of uploading the image to server the image stenography process will be done. A fter the image being uploaded into the social sites it will be appeared to other users as usually. After that if other user downloads and tries uploading the same image without original users permission, the activity cannot be performed and the MAC address of the user will be blocked. Thus the concept is explained in the above diagram. In order to improve its quality, the internal denoising based on block matching and 3-D filtering is fulfilled right after pseudo analog demodulation. The digital decoding and analog demodulation chains are basically the reverse of their transmit chains, respectively. In the second part, shown in yel OW shaded block, the feature such as scale-invariant feature transform is extracted from the recovered down-samp how similar of these correlated images are closely related ed image, then the SIFT feature is used to retrieve correlated image in cloud database. How many correlated images and to performance. In the third part, shown in gray shaded block, the correlated images are used as side information to perform the second Coset decoding to reconstruct the whole image. In order to improve image quality, the BM3D based internal denoising is used at the front end, and the correlated images are further utilized to perform external denoising at the back end.

Figure 53 The above given is the architecture diagram. It describes that how the images are watermarked and uploaded in the social network sites. In which the original user will watermark the image and upload it in the social network. While the admin will access details of the images that have been stored in the server i.e. the social network sites. During the process of uploading the image to server the image stenography process will be done. A fter the image being uploaded into the social sites it will be appeared to other users as usually. After that if other user downloads and tries uploading the same image without original users permission, the activity cannot be performed and the MAC address of the user will be blocked. Thus the concept is explained in the above diagram. In order to improve its quality, the internal denoising based on block matching and 3-D filtering is fulfilled right after pseudo analog demodulation. The digital decoding and analog demodulation chains are basically the reverse of their transmit chains, respectively. In the second part, shown in yel OW shaded block, the feature such as scale-invariant feature transform is extracted from the recovered down-samp how similar of these correlated images are closely related ed image, then the SIFT feature is used to retrieve correlated image in cloud database. How many correlated images and to performance. In the third part, shown in gray shaded block, the correlated images are used as side information to perform the second Coset decoding to reconstruct the whole image. In order to improve image quality, the BM3D based internal denoising is used at the front end, and the correlated images are further utilized to perform external denoising at the back end.

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Fig. 1: Block Diagram of proposed SFIC SFIC, pixel values of the R, G, and B components in the integer format are not converted to Y CbCr. This is eliminated as the reverse conversion from float to integer values is not taking place. In the next step, the encoding takes place. There are three stages in the encoder. In the first stage, the pixel values, hereafter named as symb, and their frequency of occurrence in each component, hereafter named as ncounta is calculated. symb, and ncounta of pixels in each component is obtained using algorithm 1. In algorithm 1, x,(i,j), the R,G,B components of the image is used as the input and seq_vectora, which is the column wise representation of x,(i,j) is found. In the next step, the pixel values are sorted and stored in symb,.This is found by extracting the minimum to maximum values of pixels in seq_vector;. In the next step of algorithm 1, the frequency of occurrence of each pixel in symb, is obtained by histogram analysis and is stored as ncount,. The overall frequency based coding scheme takes place in algorithm 2, which is summarized as below. Algorithm 1 Calculation of symbols and count
Fig. 1: Block Diagram of proposed SFIC SFIC, pixel values of the R, G, and B components in the integer format are not converted to Y CbCr. This is eliminated as the reverse conversion from float to integer values is not taking place. In the next step, the encoding takes place. There are three stages in the encoder. In the first stage, the pixel values, hereafter named as symb, and their frequency of occurrence in each component, hereafter named as ncounta is calculated. symb, and ncounta of pixels in each component is obtained using algorithm 1. In algorithm 1, x,(i,j), the R,G,B components of the image is used as the input and seq_vectora, which is the column wise representation of x,(i,j) is found. In the next step, the pixel values are sorted and stored in symb,.This is found by extracting the minimum to maximum values of pixels in seq_vector;. In the next step of algorithm 1, the frequency of occurrence of each pixel in symb, is obtained by histogram analysis and is stored as ncount,. The overall frequency based coding scheme takes place in algorithm 2, which is summarized as below. Algorithm 1 Calculation of symbols and count
Table I Compression Ratio (lossless) of standard images using proposed SFIC for different frequency factor, y. Table II PSNR for images using proposed SFIC lossless image compression scheme for different frequency factor, y.
Table I Compression Ratio (lossless) of standard images using proposed SFIC for different frequency factor, y. Table II PSNR for images using proposed SFIC lossless image compression scheme for different frequency factor, y.
The above table II shows the PSNR for the standard images using the proposed SFIC image compression scheme (lossless) for different frequency factor, y. When y = 10, the highest PSNR value is 38 for the Airplane image. The least PSNR value is 25 exhibited by Lena when y = 10. For the frequency factors (y) 20, 30, 40, 50, 100, or 110, the PSNR value for Lena is 44 and Jelly beans is 50. This is because the symbols in the symbol table do not differ much at these values of y and hence there is no difference between reconstructed images for these values of y. the
The above table II shows the PSNR for the standard images using the proposed SFIC image compression scheme (lossless) for different frequency factor, y. When y = 10, the highest PSNR value is 38 for the Airplane image. The least PSNR value is 25 exhibited by Lena when y = 10. For the frequency factors (y) 20, 30, 40, 50, 100, or 110, the PSNR value for Lena is 44 and Jelly beans is 50. This is because the symbols in the symbol table do not differ much at these values of y and hence there is no difference between reconstructed images for these values of y. the
Table III MSE for images using proposed SFIC lossless image compression scheme for different frequency factor, y.
Table III MSE for images using proposed SFIC lossless image compression scheme for different frequency factor, y.
The above table III shows the MSE for the images using the proposed SFIC image compression scheme (lossless) for different frequency factor, y. MSE is inversely proportional to PSNR and this is reflected in table III. When y is 20, 30, 40, 50, 100 and 110 for Peppers, Airplane and House, MSE is 0. Table IV Test Images and Reconstructed Images with PSNR(lossless SFIC)
The above table III shows the MSE for the images using the proposed SFIC image compression scheme (lossless) for different frequency factor, y. MSE is inversely proportional to PSNR and this is reflected in table III. When y is 20, 30, 40, 50, 100 and 110 for Peppers, Airplane and House, MSE is 0. Table IV Test Images and Reconstructed Images with PSNR(lossless SFIC)
Table V Compression Ratio lossy of standard images using proposed SFIC for different frequency factor, y. The analysis work confirms that the selection of frequency factor plays vital role in compression. The y has a great influence on the performance of SFIC. Further, the experiment results shows that SFIC gives maximum average compression ratio (12.44) when the frequency factor, y is 40.
Table V Compression Ratio lossy of standard images using proposed SFIC for different frequency factor, y. The analysis work confirms that the selection of frequency factor plays vital role in compression. The y has a great influence on the performance of SFIC. Further, the experiment results shows that SFIC gives maximum average compression ratio (12.44) when the frequency factor, y is 40.
The above table VI shows the PSNR for the standard images using the proposed SFIC image compression scheme (lossy) for different frequency factor, y. When y = 10, the highest PSNR value is 35 for the Airplane and Jelly beans images. The least PSNR value is 26 exhibited by Lena when y = 10. For the freq 110, the PSNR valu is 59, House and Jel in the symbol table d uency factors (y) 20, 30, 40, 50, 100, or e for Lena is 42, Peppers is 47, Airplane y beans is 44. This is because the symbols o not differ much at these values of y and hence there is no diff for these values of y. erence between the reconstructed images Table VI PSNR (lossy) for images using proposed SFIC image compression scheme for different frequency factor, y.
The above table VI shows the PSNR for the standard images using the proposed SFIC image compression scheme (lossy) for different frequency factor, y. When y = 10, the highest PSNR value is 35 for the Airplane and Jelly beans images. The least PSNR value is 26 exhibited by Lena when y = 10. For the freq 110, the PSNR valu is 59, House and Jel in the symbol table d uency factors (y) 20, 30, 40, 50, 100, or e for Lena is 42, Peppers is 47, Airplane y beans is 44. This is because the symbols o not differ much at these values of y and hence there is no diff for these values of y. erence between the reconstructed images Table VI PSNR (lossy) for images using proposed SFIC image compression scheme for different frequency factor, y.
Table VII Comparison of Compression Ratio (CR), PSNR of SFIC and AMBTC-QTBO. In the above table VII, the proposed image coding scheme (lossless and lossy technique) is compared with Quadtree- segmented AMBTC with Bit Map Omission (AMBTC- QTBO) and the results are analyzed. The average CR using SFIC in lossless scheme is 10.68, whereas with lossy scheme is 11.31. This increase in CR is due to the presence of quantization step in lossy scheme. In the case of AMBTC- QTBO, the average CR is 5.32. These results indicate that the CR obtained by proposed lossless and lossy scheme is more than twice the CR obtained by AMBTC-QTBO. The average PSNR value with SFIC in lossless scheme is infinity whereas with SFIC in lossy scheme is 48. The reduction in the PSNR value in the case of lossy scheme is justifiable as there is loss of data due to quantization in the reconstructed image. In the case of AMBTC- QTBO, PSNR is 32. This is far less than the PSNR obtained by SFIC in both lossless and lossy scheme. From the analysis it is seen that SFIC image compression scheme with lossless and lossy techniques outperforms AMBTC- QTBO.
Table VII Comparison of Compression Ratio (CR), PSNR of SFIC and AMBTC-QTBO. In the above table VII, the proposed image coding scheme (lossless and lossy technique) is compared with Quadtree- segmented AMBTC with Bit Map Omission (AMBTC- QTBO) and the results are analyzed. The average CR using SFIC in lossless scheme is 10.68, whereas with lossy scheme is 11.31. This increase in CR is due to the presence of quantization step in lossy scheme. In the case of AMBTC- QTBO, the average CR is 5.32. These results indicate that the CR obtained by proposed lossless and lossy scheme is more than twice the CR obtained by AMBTC-QTBO. The average PSNR value with SFIC in lossless scheme is infinity whereas with SFIC in lossy scheme is 48. The reduction in the PSNR value in the case of lossy scheme is justifiable as there is loss of data due to quantization in the reconstructed image. In the case of AMBTC- QTBO, PSNR is 32. This is far less than the PSNR obtained by SFIC in both lossless and lossy scheme. From the analysis it is seen that SFIC image compression scheme with lossless and lossy techniques outperforms AMBTC- QTBO.
Fig. 1 presents the hardware operating circuit schematic. Figure 1. Hardware schematic.
Fig. 1 presents the hardware operating circuit schematic. Figure 1. Hardware schematic.
TABLE I. RATIO OF USER INPUT AND REJECTION TO GENERATE FAR % RATES. - FRR: to characterize the system's FRR rates the ten users made 30 attempts to enter the system.
TABLE I. RATIO OF USER INPUT AND REJECTION TO GENERATE FAR % RATES. - FRR: to characterize the system's FRR rates the ten users made 30 attempts to enter the system.
TABLE II. RATIO OF USER INPUT AND REJECTION TO GENERATE FRR % RATES.
TABLE II. RATIO OF USER INPUT AND REJECTION TO GENERATE FRR % RATES.
TABLE III. EER VALUES BASED ON FAR AND FRR RATES. Two hundred samples were collected per user, these samples were collected on three distinct days on morning and afternoon. These samples were saved in text files. - EER: the methodology for obtaining the EER value was based in [11], where the threshold value of each user is defined by the crossing point between the FRR and FAR rates, thus obtaining the value of EER.
TABLE III. EER VALUES BASED ON FAR AND FRR RATES. Two hundred samples were collected per user, these samples were collected on three distinct days on morning and afternoon. These samples were saved in text files. - EER: the methodology for obtaining the EER value was based in [11], where the threshold value of each user is defined by the crossing point between the FRR and FAR rates, thus obtaining the value of EER.
This section discuss the proposed solution on_ tradi- ional/static approach for Malcode detection using intrusion Jetection system. Malicious code type of attack is unpre- dictable and dynamic, so a good study and techniques are needed to measure and create an advance way to detect and prevent the network from been compromised. This framework is presented in Figure 1 shows the steps and techniques ‘Oo detect, collect data, analysis, evaluate and validate the research. A. Network Intrusion Detection System (IDS) Snort is an open source Network Intrusion Detection System (NIDS), and has given researchers the privilege to modify and extend the system and its functionality[20][50]. NIDS is a sys- tem that monitors and detect network intrusion activities, mis- use of systems from internal or external networks. The NIDS monitors networks and servers to detect intrusion activities[1]. A few attack can start and reside within the system or focus on outside-system target, that is an intrusion attack that executes it program from outside system servers[14][9]. Snort has the capacity to analyze real-time traffic and data flow in network[51]. It is a small, lightweight IDS written by Martin Roesch. In this paper it will be use to evaluate the performance of IOCRule over the traditional detection technique. Basically it consists of the following major components as shown in Figure 2[38].
This section discuss the proposed solution on_ tradi- ional/static approach for Malcode detection using intrusion Jetection system. Malicious code type of attack is unpre- dictable and dynamic, so a good study and techniques are needed to measure and create an advance way to detect and prevent the network from been compromised. This framework is presented in Figure 1 shows the steps and techniques ‘Oo detect, collect data, analysis, evaluate and validate the research. A. Network Intrusion Detection System (IDS) Snort is an open source Network Intrusion Detection System (NIDS), and has given researchers the privilege to modify and extend the system and its functionality[20][50]. NIDS is a sys- tem that monitors and detect network intrusion activities, mis- use of systems from internal or external networks. The NIDS monitors networks and servers to detect intrusion activities[1]. A few attack can start and reside within the system or focus on outside-system target, that is an intrusion attack that executes it program from outside system servers[14][9]. Snort has the capacity to analyze real-time traffic and data flow in network[51]. It is a small, lightweight IDS written by Martin Roesch. In this paper it will be use to evaluate the performance of IOCRule over the traditional detection technique. Basically it consists of the following major components as shown in Figure 2[38].
ft) L-£Ol LTONEYPOL OVStEML, I-FOUt Is d HDONCYPOl systeii Ulat is based on docker, which includes dockerized versions of the following honey pots :- conpot, cowire, dionaea, elasticpot, emobility, glastopf and honeytrap. T -Pot uses suricata a Network Security Monitoring engine and the ELK stack to beautifully visualize all the events captured by T-Pot. T- Pot is based on Ubuntu Server 14.04.4 LTS. The honeypot daemons as well as other support components being used have been paravirtualized using docker. This allowed the run of multiple honeypot daemons on the same network interface without problems and it make the entire system very low maintenance[31][3]. The encapsulation of honeypot daemons in docker provides a good isolation of the runtime environ- ments and easy update mechanisms. This honeypot system comprises of different detection engine and types of honeypot such as low, high interaction honeypot system. In this paper T-Pot will be used to collect intrusion packets in the network. T-Pot as the honeypot type for this project is chosen from other types of honeypot because its availability, easy installation. Figure 3 shows the architecture of T-Pot. Figure 3. T-Pot Architecture
ft) L-£Ol LTONEYPOL OVStEML, I-FOUt Is d HDONCYPOl systeii Ulat is based on docker, which includes dockerized versions of the following honey pots :- conpot, cowire, dionaea, elasticpot, emobility, glastopf and honeytrap. T -Pot uses suricata a Network Security Monitoring engine and the ELK stack to beautifully visualize all the events captured by T-Pot. T- Pot is based on Ubuntu Server 14.04.4 LTS. The honeypot daemons as well as other support components being used have been paravirtualized using docker. This allowed the run of multiple honeypot daemons on the same network interface without problems and it make the entire system very low maintenance[31][3]. The encapsulation of honeypot daemons in docker provides a good isolation of the runtime environ- ments and easy update mechanisms. This honeypot system comprises of different detection engine and types of honeypot such as low, high interaction honeypot system. In this paper T-Pot will be used to collect intrusion packets in the network. T-Pot as the honeypot type for this project is chosen from other types of honeypot because its availability, easy installation. Figure 3 shows the architecture of T-Pot. Figure 3. T-Pot Architecture
C. Behaviour Analaysis and Detection
C. Behaviour Analaysis and Detection
Figure 6. Baseline Evaluation Flow
Figure 6. Baseline Evaluation Flow
Figure 7. Evaluation Framework Steps Figure 4 refers to the overall flow of the research evaluation framework. It explains the steps to be taken to achieve the objective of the thesis. This framework is explain the details in the following subsections.
Figure 7. Evaluation Framework Steps Figure 4 refers to the overall flow of the research evaluation framework. It explains the steps to be taken to achieve the objective of the thesis. This framework is explain the details in the following subsections.
Figure 8. Malware Data 1) Information Gathering: Base on _ the research framework, the information gathering can be referred as data collection. Many steps and techniques were implemented and adopted during data col sites, downloading of ma the network environment ection such as accessing ma (Honeypot) to record the file. Malware data were co from honey pot implement on the data collected, a tota was extracted from the T- ed in an isolated network. Pot honeypot system in a icious ware files and execute them in for the data collection system lected Based 1 number of 47462 malicious code period of one month and from the different network environment through the wireless network and wired network. T-Pot system provides logfile monitor system which re-checks the logfile to filter out normal traffic that was recorded as an attack. Figure 8 refers to the sample of training dataset collected. C. Data Analysis
Figure 8. Malware Data 1) Information Gathering: Base on _ the research framework, the information gathering can be referred as data collection. Many steps and techniques were implemented and adopted during data col sites, downloading of ma the network environment ection such as accessing ma (Honeypot) to record the file. Malware data were co from honey pot implement on the data collected, a tota was extracted from the T- ed in an isolated network. Pot honeypot system in a icious ware files and execute them in for the data collection system lected Based 1 number of 47462 malicious code period of one month and from the different network environment through the wireless network and wired network. T-Pot system provides logfile monitor system which re-checks the logfile to filter out normal traffic that was recorded as an attack. Figure 8 refers to the sample of training dataset collected. C. Data Analysis
Figure 1. Example of a humanoid robot for communication research result has been put to practical use as a part of products, and it is possible for ordinary people to purchase it. (Figure 1) In recent, communication between people is important. Grasp of human intention in communication is very important. In order to contact people well, in our daily lives we discriminate between lies and joke consciously or unconsciously from the change in expression and voice pitch accompanying utterances.
Figure 1. Example of a humanoid robot for communication research result has been put to practical use as a part of products, and it is possible for ordinary people to purchase it. (Figure 1) In recent, communication between people is important. Grasp of human intention in communication is very important. In order to contact people well, in our daily lives we discriminate between lies and joke consciously or unconsciously from the change in expression and voice pitch accompanying utterances.
Figure 2. Example of a figure caption. (figure caption) B. Picking up color
Figure 2. Example of a figure caption. (figure caption) B. Picking up color
value of the position of the pixel of the feature point. Figure 3. Range around the detection point of Red, X is the coordinate of the horizontal pixel, y is the coordinate of the vertical pixel, and R (x, y) is the red luminance
value of the position of the pixel of the feature point. Figure 3. Range around the detection point of Red, X is the coordinate of the horizontal pixel, y is the coordinate of the vertical pixel, and R (x, y) is the red luminance
LEI. PROPERTIES OF NOISES TO BE CONSIDERED noise. We also consider removing these noises. Table 1 summarizes what is considered noise.
LEI. PROPERTIES OF NOISES TO BE CONSIDERED noise. We also consider removing these noises. Table 1 summarizes what is considered noise.
As a preliminary experiment, processing up to this point was performed for all 108 parts of face parts positions detected. As a result, the four points of eye holes (eye socket), right cheek, left cheek and jaw, which are shown in figure 5, have the largest dispersion of R luminance values. Then only these four points were used afterwards. These points numbers are No. 24, 39, 74, and 103, respectively. As a preliminary experiment, processing up to this point was performed for all 108 parts of face parts positions detected. As a is frequency of input signal.
As a preliminary experiment, processing up to this point was performed for all 108 parts of face parts positions detected. As a result, the four points of eye holes (eye socket), right cheek, left cheek and jaw, which are shown in figure 5, have the largest dispersion of R luminance values. Then only these four points were used afterwards. These points numbers are No. 24, 39, 74, and 103, respectively. As a preliminary experiment, processing up to this point was performed for all 108 parts of face parts positions detected. As a is frequency of input signal.
Figure 5. Four feature points for picking up luminance R. In this section, we construct a system to judge whether or not the person is lying. We propose a new indicator for lie detection In this section, we construct a system to judge whether or not the person is lying. We propose a new indicator for lie detection called ‘LieCount’. It can be confirmed that the red luminance value is lower when the person is lying than when not lying. For this time, we aim to detect lies using this property. However, although it is ideal that it is possible to output the detection result of the ie for each frame in real time, since the luminance value is acquired by using the camera, there is a problem that measurement noise will inevitably occur every frame. That is, if itis tried to judge whether it is a lie or not for each frame, there is a possibility that the ie detection result will be greatly changed by the measurement noise. Therefore, since the number of frames for one section is 600 frames, a lie is detected by utilizing the opportunity to judge whether it is 600 times or not. That is, if the red luminance value of one unit in one unit is lower than the average of the red luminance value of one unit out of the 600 frames, when the number of the section is less than the number of the sections, the examinee lies is decided to have a high probability of being attached. Therefore, the higher the value of LieCount, the higher the likelihood that the examinee is lying. Below is the procedure of detection of lie. First, we add up and average 30 sections of lie data by equation (3), and 120 sections of true data by equation (4).
Figure 5. Four feature points for picking up luminance R. In this section, we construct a system to judge whether or not the person is lying. We propose a new indicator for lie detection In this section, we construct a system to judge whether or not the person is lying. We propose a new indicator for lie detection called ‘LieCount’. It can be confirmed that the red luminance value is lower when the person is lying than when not lying. For this time, we aim to detect lies using this property. However, although it is ideal that it is possible to output the detection result of the ie for each frame in real time, since the luminance value is acquired by using the camera, there is a problem that measurement noise will inevitably occur every frame. That is, if itis tried to judge whether it is a lie or not for each frame, there is a possibility that the ie detection result will be greatly changed by the measurement noise. Therefore, since the number of frames for one section is 600 frames, a lie is detected by utilizing the opportunity to judge whether it is 600 times or not. That is, if the red luminance value of one unit in one unit is lower than the average of the red luminance value of one unit out of the 600 frames, when the number of the section is less than the number of the sections, the examinee lies is decided to have a high probability of being attached. Therefore, the higher the value of LieCount, the higher the likelihood that the examinee is lying. Below is the procedure of detection of lie. First, we add up and average 30 sections of lie data by equation (3), and 120 sections of true data by equation (4).
In this paper, we conducted an experiment with reference to the method of card inspection of polygraph inspection. The the chin is placed on the stand, and the examinee carries out the experiment by placing his/ her head on this chin rest. The environment
In this paper, we conducted an experiment with reference to the method of card inspection of polygraph inspection. The the chin is placed on the stand, and the examinee carries out the experiment by placing his/ her head on this chin rest. The environment
Of the five prepared cards, we define "cards of lies" for cards picked up by the examinees, and four other cards as "cards of truth". In addition, we chose "Lie's card" and "Truth card" to present so that the numbers and marks will vary. The reason for this is that the examinees misunderstand if they have the same numbers or the same marks. At the beginning of the experiment, six pieces of cards not related at all to experiments which were not "cards of lies" and "cards of truth" were presented to the display continuously. In this research, we refer to cards that have no relation to this experiment as dummy card to cool down. The reason for presenting
Of the five prepared cards, we define "cards of lies" for cards picked up by the examinees, and four other cards as "cards of truth". In addition, we chose "Lie's card" and "Truth card" to present so that the numbers and marks will vary. The reason for this is that the examinees misunderstand if they have the same numbers or the same marks. At the beginning of the experiment, six pieces of cards not related at all to experiments which were not "cards of lies" and "cards of truth" were presented to the display continuously. In this research, we refer to cards that have no relation to this experiment as dummy card to cool down. The reason for presenting
nis card is that in the case of examinee who are not accustomed to experimen ts, the physiological response is not stable from the ansion at which the experiment starts. Even in an actual polygraph examination, we are doing some questions first to calm th xaminee. In this research, this is defined as ‘1 unit’ with one card of lie and ummy card is inserted. The reason for presenting is to prevent the influence o 4 cards of truth. Between the unit and the unit, | f the previous unit from continuing to the next unit )ne experiment with all units is called ’1 set’ in this paper. The playing proced reen, blue, red cards mean dummy, true, lie cards, respectively. ure of the cards is shown in figure 8. In this figure
nis card is that in the case of examinee who are not accustomed to experimen ts, the physiological response is not stable from the ansion at which the experiment starts. Even in an actual polygraph examination, we are doing some questions first to calm th xaminee. In this research, this is defined as ‘1 unit’ with one card of lie and ummy card is inserted. The reason for presenting is to prevent the influence o 4 cards of truth. Between the unit and the unit, | f the previous unit from continuing to the next unit )ne experiment with all units is called ’1 set’ in this paper. The playing proced reen, blue, red cards mean dummy, true, lie cards, respectively. ure of the cards is shown in figure 8. In this figure
It means that the higher the value of LieCount calculated by the conventional method, the higher the likelihood of lying. As xpected, overall, it was confirmed that the value of LieCount when lying is larger. Especially, it is thought that examinee No.1 vas the examinee of the preliminary experiment because the difference between LieCount when examinee No.1 lied and wher 1ot lying was large. In examinee No.3, it is confirmed that LieCount is larger than that of Lie, and it can be confirmed that there s a case that the red luminance value of the eye socket, the right cheek, the left cheek, and the jaw does not decrease even when ying . Finally, there was no difference in the value of LieCount in examinee No.4, and it can be confirmed that there was nc hange in the red luminance values of the examinee around the eye socket, the right cheek, the left cheek and the jaw as well in his examinee. The average value of LieCount at the time when four of the six examinees lied led to a higher result and confirmec he possibility of LieCount’s detection of a lie. It means that the higher the value of LieCount calculated by the conventional method, the higher the likelihood of lying. As
It means that the higher the value of LieCount calculated by the conventional method, the higher the likelihood of lying. As xpected, overall, it was confirmed that the value of LieCount when lying is larger. Especially, it is thought that examinee No.1 vas the examinee of the preliminary experiment because the difference between LieCount when examinee No.1 lied and wher 1ot lying was large. In examinee No.3, it is confirmed that LieCount is larger than that of Lie, and it can be confirmed that there s a case that the red luminance value of the eye socket, the right cheek, the left cheek, and the jaw does not decrease even when ying . Finally, there was no difference in the value of LieCount in examinee No.4, and it can be confirmed that there was nc hange in the red luminance values of the examinee around the eye socket, the right cheek, the left cheek and the jaw as well in his examinee. The average value of LieCount at the time when four of the six examinees lied led to a higher result and confirmec he possibility of LieCount’s detection of a lie. It means that the higher the value of LieCount calculated by the conventional method, the higher the likelihood of lying. As
based improvement and services. Fig. 1 Shows concept of Cloud Computing and its characteristics. According to National Institute of Standards and Technology (NIST) one of the most accepted definition of cloud computing is [33], “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to |
based improvement and services. Fig. 1 Shows concept of Cloud Computing and its characteristics. According to National Institute of Standards and Technology (NIST) one of the most accepted definition of cloud computing is [33], “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to |
"he Service Monitoring layer is the upper layer of this proposed engineering. At that point the information send t service Monitoring for SLA checking. Once the information enter the SLA checking instruments, the informatio: onfirmation process initiate. The check procedure is a procedure where the information got from Data Collector laye ongoing information) are being contrasted and the information that expressed in the Service Level Agreement b itilizing the correlation strategy. In the event that the got information is coordinate with the SLA esteem then th yutcome demonstrate "Ok". In any case, if the information is not coordinate then the outcome indicate "Violate". The Service Monitoring layer is the upper layer of this proposed engineering. At that point the information send to identified with the objective administration. All the need information enter the Service Monitoring to prepared. All state markers characterize an arrangement of Service Level Objectives (SLO) which can be indicated in the SLAs
"he Service Monitoring layer is the upper layer of this proposed engineering. At that point the information send t service Monitoring for SLA checking. Once the information enter the SLA checking instruments, the informatio: onfirmation process initiate. The check procedure is a procedure where the information got from Data Collector laye ongoing information) are being contrasted and the information that expressed in the Service Level Agreement b itilizing the correlation strategy. In the event that the got information is coordinate with the SLA esteem then th yutcome demonstrate "Ok". In any case, if the information is not coordinate then the outcome indicate "Violate". The Service Monitoring layer is the upper layer of this proposed engineering. At that point the information send to identified with the objective administration. All the need information enter the Service Monitoring to prepared. All state markers characterize an arrangement of Service Level Objectives (SLO) which can be indicated in the SLAs
framework from . Here, we determined the most frequent attributes that applied in SLA. Table I shows the SLA cloud services provided is following the contract or not. Itis very important in terms of reliability, security and avail-
framework from . Here, we determined the most frequent attributes that applied in SLA. Table I shows the SLA cloud services provided is following the contract or not. Itis very important in terms of reliability, security and avail-
Figure 3. CPU speed and usage of storage varies with time. values changes per minutes. Load value has increased at high time.
Figure 3. CPU speed and usage of storage varies with time. values changes per minutes. Load value has increased at high time.
Figure 4. Scale up and down varies with time. icant SLAs and each pertinent SLA prompts the outflow of a SLC result notice. Ne consider the parameters value at several load such as heavy load, average load and low load. Load values varies n different situations such as no. of customer, availability of resources, and usage of CPU at different time. Fig. 6 lemonstrate that all the average times spent are very low. The green diagram demonstrates comes about acquired vhen just "OK" SLC comes about are delivered, while the yellow chart indicates comes about got when SLC produces ne "Violation" and some "Ok". As it can be seen, every one of the circumstances spent are low when just 1 pertinent SLA is included to almost 1 second when 7 important SLAs are included. Of course, the normal time stays low and levelops with the quantity of SLAs included. As an update, each report got by the SLC is checked with all the signif- cant SLAs and each pertinent SLA prompts the outflow of a SLC result notice. develops with the quantity of SLAs included. As an update, each report got by the SLC is checked with all the signif-
Figure 4. Scale up and down varies with time. icant SLAs and each pertinent SLA prompts the outflow of a SLC result notice. Ne consider the parameters value at several load such as heavy load, average load and low load. Load values varies n different situations such as no. of customer, availability of resources, and usage of CPU at different time. Fig. 6 lemonstrate that all the average times spent are very low. The green diagram demonstrates comes about acquired vhen just "OK" SLC comes about are delivered, while the yellow chart indicates comes about got when SLC produces ne "Violation" and some "Ok". As it can be seen, every one of the circumstances spent are low when just 1 pertinent SLA is included to almost 1 second when 7 important SLAs are included. Of course, the normal time stays low and levelops with the quantity of SLAs included. As an update, each report got by the SLC is checked with all the signif- cant SLAs and each pertinent SLA prompts the outflow of a SLC result notice. develops with the quantity of SLAs included. As an update, each report got by the SLC is checked with all the signif-
J Figure 5. Response Time varies with time. Figure 6. Average time spent according to the number of SLA involved.
J Figure 5. Response Time varies with time. Figure 6. Average time spent according to the number of SLA involved.
load values for SLA parameters.
load values for SLA parameters.
Figure 8. Comparison results between SLA values and actual values in low load Figure 7. Comparison results between SLA values and actual values in heavy and average load Based on the fig 7, 8 it is clearly shown that if the actual value lower than the SLA value then the SLA is violated. In heavy load, it is shown that there have 4 violated SLA and 9 with not violated.so, here maximum SLA’s are violated and follow the contract. In average load, it is shown that maximum SLA‘s values are higher than actual value. The verification results are used to inform the target service administrator to make decisions regarding reconfiguration or to terminate a contract. This result also notify the clients. Based on the fig 7, 8 itis clearly shown that if the actual value lower than the SLA value then the SLA is violated. In
Figure 8. Comparison results between SLA values and actual values in low load Figure 7. Comparison results between SLA values and actual values in heavy and average load Based on the fig 7, 8 it is clearly shown that if the actual value lower than the SLA value then the SLA is violated. In heavy load, it is shown that there have 4 violated SLA and 9 with not violated.so, here maximum SLA’s are violated and follow the contract. In average load, it is shown that maximum SLA‘s values are higher than actual value. The verification results are used to inform the target service administrator to make decisions regarding reconfiguration or to terminate a contract. This result also notify the clients. Based on the fig 7, 8 itis clearly shown that if the actual value lower than the SLA value then the SLA is violated. In
The package Ambari is the open source installation and management system for Hadoop Platforms.
The package Ambari is the open source installation and management system for Hadoop Platforms.
Figure 2: Work Flow of Forensic Research and Investigation on Targeted Hadoop Platform generating evidences to embody the criminal activity. the illegal usages of criminal by applying the forensic readiness. relevant evidence. Exposing residual artifacts among the sheer amount of backlogs can reduce the volume for
Figure 2: Work Flow of Forensic Research and Investigation on Targeted Hadoop Platform generating evidences to embody the criminal activity. the illegal usages of criminal by applying the forensic readiness. relevant evidence. Exposing residual artifacts among the sheer amount of backlogs can reduce the volume for
Table 2: Residual Artifacts (Forensically important Log Files) of Targeted Hadoop Platform Among the large amount of log files, the contents in namenode.log, syslog, blk ##4HHE job-##HHHH dfs-auditlog, hue-access.log files are useful for forensic environment. The contents of log file are able tc ncover the criminal activity. Among them, the forensically complete logs which can embody the crime scene re hue-access.log and hdfs-audit.log. The criminal activity can be embodied by applying only these two loc ‘les even excluding other backlogs. In the configuration files for handling the logs, we can adjust the maximurr mount of log. If the maximum amount is occupied, the old files are made as backup log files. The followinc ‘able 2 expresses the location and configuration files that store the variables and default location of these two —
Table 2: Residual Artifacts (Forensically important Log Files) of Targeted Hadoop Platform Among the large amount of log files, the contents in namenode.log, syslog, blk ##4HHE job-##HHHH dfs-auditlog, hue-access.log files are useful for forensic environment. The contents of log file are able tc ncover the criminal activity. Among them, the forensically complete logs which can embody the crime scene re hue-access.log and hdfs-audit.log. The criminal activity can be embodied by applying only these two loc ‘les even excluding other backlogs. In the configuration files for handling the logs, we can adjust the maximurr mount of log. If the maximum amount is occupied, the old files are made as backup log files. The followinc ‘able 2 expresses the location and configuration files that store the variables and default location of these two —
file. The Figure 2 shows the parameters and values of a record (entry) in hue-access.log. resolution into Hadoop operations. Its configuration is presented in the log4j properties. By default, the log4j.properties file has the log threshold set to WARN. By setting this level to INFO, audit logging can be
file. The Figure 2 shows the parameters and values of a record (entry) in hue-access.log. resolution into Hadoop operations. Its configuration is presented in the log4j properties. By default, the log4j.properties file has the log threshold set to WARN. By setting this level to INFO, audit logging can be
The number of the sets in each cluster is calculated in Eq1 and Eq2. The Eq3 describes the value of significant parameter. The output of the algorithm is the clusters which form as a corpus of potential evidence. This model applies the proposed algorithm; Cluster Analysis Algorithm to group the data by each Eqi: number of Cluster N(C) = number of Parameter in Dataset N (P)
The number of the sets in each cluster is calculated in Eq1 and Eq2. The Eq3 describes the value of significant parameter. The output of the algorithm is the clusters which form as a corpus of potential evidence. This model applies the proposed algorithm; Cluster Analysis Algorithm to group the data by each Eqi: number of Cluster N(C) = number of Parameter in Dataset N (P)
Figure 5: Log Analysis Model for Implementing Forensic Readiness System data itself. The contents of hue-access.log and hdfs-audit.log files are collected for analysis in a timely manner
Figure 5: Log Analysis Model for Implementing Forensic Readiness System data itself. The contents of hue-access.log and hdfs-audit.log files are collected for analysis in a timely manner
Figure1. High Level Architecture of MSABDP scalability, cost effective, and parallel processing by using Hadoop platform. 3.1 Data Ingestion Layer
Figure1. High Level Architecture of MSABDP scalability, cost effective, and parallel processing by using Hadoop platform. 3.1 Data Ingestion Layer
classified using the model in order to produce the classified tweets. The process flow of the proposed system is described in Figure 2.
classified using the model in order to produce the classified tweets. The process flow of the proposed system is described in Figure 2.
Table 1: Sample Emoticons and their Sentiment Values abbreviations can be seen in Table 2. Table 2: Sample abbreviations and their expansion
Table 1: Sample Emoticons and their Sentiment Values abbreviations can be seen in Table 2. Table 2: Sample abbreviations and their expansion
Instead of manual labeling the class, SentiStrength lexicon based approach is used for annotating the training data of the learning-based classifier. SentiStrength [16], a lexicon-based classifier, uses additional (non-lexical) linguistic information and rules to detect sentiment strength in short informal English text. The contextual valence shifter: negation and intensifier are used to evaluate the context sentiment and to solve the context dependent problem by applying NegationWordList and BoosterWordList. There are consists of eight dictionaries: BoosterW ordList, EmoticonLookupTable, EmotionLookupTable, EnglishWordList, IdionLookupTable, NegationWordList, QuestionW ords and slangLookupT able. Instead of manual labeling the class, SentiStrength lexicon based approach is used for annotating the training data
Instead of manual labeling the class, SentiStrength lexicon based approach is used for annotating the training data of the learning-based classifier. SentiStrength [16], a lexicon-based classifier, uses additional (non-lexical) linguistic information and rules to detect sentiment strength in short informal English text. The contextual valence shifter: negation and intensifier are used to evaluate the context sentiment and to solve the context dependent problem by applying NegationWordList and BoosterWordList. There are consists of eight dictionaries: BoosterW ordList, EmoticonLookupTable, EmotionLookupTable, EnglishWordList, IdionLookupTable, NegationWordList, QuestionW ords and slangLookupT able. Instead of manual labeling the class, SentiStrength lexicon based approach is used for annotating the training data
Reduce stage outputs the results obtained by the Mappers.
Reduce stage outputs the results obtained by the Mappers.
procedure for developing Model II is illustrated in Figured.
procedure for developing Model II is illustrated in Figured.
“Neg” are Classified into two classes: “negative” and “strongly negative”. Procedure: Developing Classification Model I
“Neg” are Classified into two classes: “negative” and “strongly negative”. Procedure: Developing Classification Model I
8.2. Classification by Developed model
8.2. Classification by Developed model
Figure6. Procedure for Developing Classification Model III The three trained models are used to classify the new instances. For classification, naive bayes classifier uses probabilities to decide which class best matches for a given input text. Word id and tfidf weight are used to create vector for the new tweet. The naive bayes classifier is classified by using the vector. For model I, the score of three class label is calculated. The “bestscore” is set to “-Double- MAX VALUE”. The “bestcategoryld” is set to “-1” and “categoryld” is set to index of classification result. If the indexed score is greater than “bestscore”, bestcategoryld is “ce replaced with categoryld. If the “bestcategoryld” is equal with “1”, the classifier classify as “Pos”. If the “bestcategoryld” is equal with “0”, the classifier classify as “Neu”. Otherwise, it classify as “Neg”. For model II,
Figure6. Procedure for Developing Classification Model III The three trained models are used to classify the new instances. For classification, naive bayes classifier uses probabilities to decide which class best matches for a given input text. Word id and tfidf weight are used to create vector for the new tweet. The naive bayes classifier is classified by using the vector. For model I, the score of three class label is calculated. The “bestscore” is set to “-Double- MAX VALUE”. The “bestcategoryld” is set to “-1” and “categoryld” is set to index of classification result. If the indexed score is greater than “bestscore”, bestcategoryld is “ce replaced with categoryld. If the “bestcategoryld” is equal with “1”, the classifier classify as “Pos”. If the “bestcategoryld” is equal with “0”, the classifier classify as “Neu”. Otherwise, it classify as “Neg”. For model II,
Table3. Experiment Parameters The specifications of devices and necessary software component of proposed system are described in table 3. In the experiment, the cluster is composed of 4 computing nodes (VMs) with one name node and three data
Table3. Experiment Parameters The specifications of devices and necessary software component of proposed system are described in table 3. In the experiment, the cluster is composed of 4 computing nodes (VMs) with one name node and three data
Figure8. Comparison of SentiStrength lexicon based classification and Manual Classification for iphone 1% in strongly negative, and 7 % in neutral. Therefore, the overall accuracy rate is 82%and error rate is 18% for inhone data
Figure8. Comparison of SentiStrength lexicon based classification and Manual Classification for iphone 1% in strongly negative, and 7 % in neutral. Therefore, the overall accuracy rate is 82%and error rate is 18% for inhone data
Figure9. Comparison of SentiStrength Lexicon based Classification and M anual Classification for samsung The comparative results of SentiStrength lexicon based classification and manual classification for samsung is illustrated in Figure 9. The results show the tweets Percentage of classification with SentiStrength based approach for positive, strongly positive, negative, strongly negative, neutral class labels are 20, 5, 16, 6 and 53. And manual classified Tweets percentages for positive, strongly positive, negative, strongly_negative, neutral are 24, 8, 11, 8, and 49. Therefore, error rate for lexicon based classification is 5% in positive, 3% in strongly positive and 2% in
Figure9. Comparison of SentiStrength Lexicon based Classification and M anual Classification for samsung The comparative results of SentiStrength lexicon based classification and manual classification for samsung is illustrated in Figure 9. The results show the tweets Percentage of classification with SentiStrength based approach for positive, strongly positive, negative, strongly negative, neutral class labels are 20, 5, 16, 6 and 53. And manual classified Tweets percentages for positive, strongly positive, negative, strongly_negative, neutral are 24, 8, 11, 8, and 49. Therefore, error rate for lexicon based classification is 5% in positive, 3% in strongly positive and 2% in
Table4. Comparison of single-tier and multi-tier result for samsung Table3. Comparison of single-tier and multi-tier result for iphone models. The global data may contain the misclassified instances from all of the hierarchical classification models.
Table4. Comparison of single-tier and multi-tier result for samsung Table3. Comparison of single-tier and multi-tier result for iphone models. The global data may contain the misclassified instances from all of the hierarchical classification models.
Table 3 shows the comparative results of single-tier and multi-tier classification for iphone. The results show a
Table 3 shows the comparative results of single-tier and multi-tier classification for iphone. The results show a
higher accuracy than multi-tier classification (with global data) and single-teir with 4% and 6% respectively. classification (with reference data) is higher than single-teir with 7% and the multi-tier classification (with global
higher accuracy than multi-tier classification (with global data) and single-teir with 4% and 6% respectively. classification (with reference data) is higher than single-teir with 7% and the multi-tier classification (with global
Figure 1. Login interface to enter grid size [13]
Figure 1. Login interface to enter grid size [13]
password. The registration interface is shown in Figure 3. During Login, the user enters the encrypted password for authentication. The code for encryption is sent to user’s mail-id as well as Mobile number. First, the user has to enter his/her user name first and then he/she has to generate a pass-code for encryption. When the user generates the pass-code, the proposed scheme sends the pass-code to user email-id as well as his/her mobile number automatically. Basing on the pass-code, the user has to select the images as his/her password. The login interface is shown in Figure 4. the user details are registered. The details include user name, e-mail ID, mobile number, and number of pictures for
password. The registration interface is shown in Figure 3. During Login, the user enters the encrypted password for authentication. The code for encryption is sent to user’s mail-id as well as Mobile number. First, the user has to enter his/her user name first and then he/she has to generate a pass-code for encryption. When the user generates the pass-code, the proposed scheme sends the pass-code to user email-id as well as his/her mobile number automatically. Basing on the pass-code, the user has to select the images as his/her password. The login interface is shown in Figure 4. the user details are registered. The details include user name, e-mail ID, mobile number, and number of pictures for
Table 1. Limits for No. of Pictures in the Password and Length of Grid Number’ represents the number of positions a user has to move from original pass picture, to select the map ass picture. ‘Alphabet’ represents the direction to move. We assume that the grid is foldable (or rollable) pplying Caesar Cipher Technique, so that the side edges, touch each other whenever necessary. i.e., first colu ill touch last column if you roll (or fold). Similarly, first row will touch last row if you fold. For example in Fig , if the pass picture is Pomegranate and the pass-code is R2, then the user has to select Zebra as his passw« icture by sliding two positions to the right. If the pass picture is Papaya and the pass-code is T1, then the user has elect Beans as his picture by moving one position up. If the pass picture is Watermelon and the pass-code is ] 1en the user has to select Cauliflower as his pass picture by moving four positions to the left. Same way if the p icture is Elephant and the pass-code is B3, then the user has to select the ‘Picture of Lady with Red Dress’ as ass picture by moving 3 positions to the bottom. Limits are set for the two parameters - number of passwi ictures and the length of the grid. pass picture by moving 3 positions to the bottom. Limits are set for the two parameters - number of password
Table 1. Limits for No. of Pictures in the Password and Length of Grid Number’ represents the number of positions a user has to move from original pass picture, to select the map ass picture. ‘Alphabet’ represents the direction to move. We assume that the grid is foldable (or rollable) pplying Caesar Cipher Technique, so that the side edges, touch each other whenever necessary. i.e., first colu ill touch last column if you roll (or fold). Similarly, first row will touch last row if you fold. For example in Fig , if the pass picture is Pomegranate and the pass-code is R2, then the user has to select Zebra as his passw« icture by sliding two positions to the right. If the pass picture is Papaya and the pass-code is T1, then the user has elect Beans as his picture by moving one position up. If the pass picture is Watermelon and the pass-code is ] 1en the user has to select Cauliflower as his pass picture by moving four positions to the left. Same way if the p icture is Elephant and the pass-code is B3, then the user has to select the ‘Picture of Lady with Red Dress’ as ass picture by moving 3 positions to the bottom. Limits are set for the two parameters - number of passwi ictures and the length of the grid. pass picture by moving 3 positions to the bottom. Limits are set for the two parameters - number of password
Let ‘N’ be the set of all possible passwords and ‘m’ be the number of pictures in the selected password of a user. Let ‘K’ be the set of all possible keys. Then,
Let ‘N’ be the set of all possible passwords and ‘m’ be the number of pictures in the selected password of a user. Let ‘K’ be the set of all possible keys. Then,
The recent Wireless cards are designed as dual. They are engineered to be faster, stronger than previous generation. Actually, it is the standard 802.1lac that is the most implemented on wireless cards, it delivers up to 3x faster Wi- Fi speeds (up to 433 Mbps) than 802.11n, with up to 3x more bandwidth per stream for more users and devices. It’s specification features implemented that improve channel
The recent Wireless cards are designed as dual. They are engineered to be faster, stronger than previous generation. Actually, it is the standard 802.1lac that is the most implemented on wireless cards, it delivers up to 3x faster Wi- Fi speeds (up to 433 Mbps) than 802.11n, with up to 3x more bandwidth per stream for more users and devices. It’s specification features implemented that improve channel
A. 1Client/traffic light
A. 1Client/traffic light
Figure 2 performance results for 1 clients
Figure 2 performance results for 1 clients
Figure 3 performance results for 5 clients When we increase the number of clients, we can observe that 802.11b suffers a huge performance degradation, the delay remains acceptable for the others standards. For the loss rate, the lowest value is obtained for 802.11a, with a minimal difference with 802.11g and 802.11n. always for delay, it is the 801.11n that gave best performances.
Figure 3 performance results for 5 clients When we increase the number of clients, we can observe that 802.11b suffers a huge performance degradation, the delay remains acceptable for the others standards. For the loss rate, the lowest value is obtained for 802.11a, with a minimal difference with 802.11g and 802.11n. always for delay, it is the 801.11n that gave best performances.
for 10 clients, we can notice that 802.11b has reached its limits. 802.11n give the best values for all the considered metrics, which is completely reasonable, due to the huge improvements that has undergone the 802.11n standard, more details are given in paragraph 3.
for 10 clients, we can notice that 802.11b has reached its limits. 802.11n give the best values for all the considered metrics, which is completely reasonable, due to the huge improvements that has undergone the 802.11n standard, more details are given in paragraph 3.
The above given figure depicts the block diagram of the receiver, which is composed of recovery of small down - sampled image, correlated image retrieval and reconstruction of received image. The first part, as shown in red dash line block, includes the digital decoding of low-frequency DCT coefficients MSB, analog demodulation of low-frequency DCT coefficients LSB, and the first Coset decoding to recover a down-sampled transmitted image. DCT block transform DCT coefficients back to pixels to facilitate internal denoising. In order to recover a small down -sampled image at receiver side to fulfill correlated image retrieval, the most significant bits for a small portion of low- frequency DCT coefficients are sent using digital modulation in upper branch. Due distribution of DCT coefficients, run- ength coding can effectively compress MSBs of low- frequency DCT coefficients. Thus they are used to increase the image quality in well defined manner.
The above given figure depicts the block diagram of the receiver, which is composed of recovery of small down - sampled image, correlated image retrieval and reconstruction of received image. The first part, as shown in red dash line block, includes the digital decoding of low-frequency DCT coefficients MSB, analog demodulation of low-frequency DCT coefficients LSB, and the first Coset decoding to recover a down-sampled transmitted image. DCT block transform DCT coefficients back to pixels to facilitate internal denoising. In order to recover a small down -sampled image at receiver side to fulfill correlated image retrieval, the most significant bits for a small portion of low- frequency DCT coefficients are sent using digital modulation in upper branch. Due distribution of DCT coefficients, run- ength coding can effectively compress MSBs of low- frequency DCT coefficients. Thus they are used to increase the image quality in well defined manner.
KBA is a method to verify the user’s identity by matching at least one piece of information through providing secret knowledge. The knowledge based authentication techniques include text passwords, graphical passwords and visual passwords [1, 4].
KBA is a method to verify the user’s identity by matching at least one piece of information through providing secret knowledge. The knowledge based authentication techniques include text passwords, graphical passwords and visual passwords [1, 4].
Fig.3. Developed Security Criteria and KBA Variables
Fig.3. Developed Security Criteria and KBA Variables
As we discussed previously (problem specification section), authentication system should provide strong scheme while maintaining users' roles. We propose that KBA scheme as multi-factors authentication method to allow users' roles while influencing them specifically admins toward stronger authentication (see figure2). In effect, this proposed KBA approach makes choosing a more secure authentication method by giving more authentication sessions and criteria in each roles specified (see figure 3).
As we discussed previously (problem specification section), authentication system should provide strong scheme while maintaining users' roles. We propose that KBA scheme as multi-factors authentication method to allow users' roles while influencing them specifically admins toward stronger authentication (see figure2). In effect, this proposed KBA approach makes choosing a more secure authentication method by giving more authentication sessions and criteria in each roles specified (see figure 3).
Quality of Service (QoS):
Quality of Service (QoS):
nine parameters are quantitative and last five parameters are qualitative.
nine parameters are quantitative and last five parameters are qualitative.
From the above table, the nine quantifiable attributes are calculated as follows [8] [9], received) in a single workload per unit time (usually in seconds).
From the above table, the nine quantifiable attributes are calculated as follows [8] [9], received) in a single workload per unit time (usually in seconds).
machines. Eg: Xen, Oracle VM, VMware ESX/ESXi, Microsoft Hyper-V. Type 2 (or) Hosted Hypervisors
machines. Eg: Xen, Oracle VM, VMware ESX/ESXi, Microsoft Hyper-V. Type 2 (or) Hosted Hypervisors
They are listed below. Table.3 Issues in Effective Management of Resource [2]
They are listed below. Table.3 Issues in Effective Management of Resource [2]
Figure 1. Example Here, the example shows the working of the GICD algorithm. For simplification of the example, the smal In the first step, we calculate the degree of all the nodes in the given graph. The degree of the node is the number f outgoing edges connected with the particular node. So if a node is connected with other three nodes, the degree of 1e node is three. In the second step, the nodes are arranged in descending order of their degrees. As the strategy is a reedy strategy, the community formation will be started from the highest degree node. The third step is to unmark 11 the nodes so that we can track which nodes are alrea ighest degree node (or nodes) is (/are) chosen and the ini dy considered and which are not. In the fourth step, the tial community is formed with only one node within it. In ie fifth step, the variable x is initialized to 1, which is used to keep track of the node number in a process and works s a counter. In the sixth step, we initialize the variable IC (Initial Community) same as the number of nodes. In the aventh step, the process of selecting the appropriate community for a particular node is done based on the arameter density [2]. By adding a particular node to a co mmunity, if the density increases, the node is included in 1e community; otherwise, it is not. The selected node is then removed from the IC. The step number seven is ontinued until all the nodes are processed and the list IC is empty. IV. ALGORITHM EXAMPLE
Figure 1. Example Here, the example shows the working of the GICD algorithm. For simplification of the example, the smal In the first step, we calculate the degree of all the nodes in the given graph. The degree of the node is the number f outgoing edges connected with the particular node. So if a node is connected with other three nodes, the degree of 1e node is three. In the second step, the nodes are arranged in descending order of their degrees. As the strategy is a reedy strategy, the community formation will be started from the highest degree node. The third step is to unmark 11 the nodes so that we can track which nodes are alrea ighest degree node (or nodes) is (/are) chosen and the ini dy considered and which are not. In the fourth step, the tial community is formed with only one node within it. In ie fifth step, the variable x is initialized to 1, which is used to keep track of the node number in a process and works s a counter. In the sixth step, we initialize the variable IC (Initial Community) same as the number of nodes. In the aventh step, the process of selecting the appropriate community for a particular node is done based on the arameter density [2]. By adding a particular node to a co mmunity, if the density increases, the node is included in 1e community; otherwise, it is not. The selected node is then removed from the IC. The step number seven is ontinued until all the nodes are processed and the list IC is empty. IV. ALGORITHM EXAMPLE
Figure 2. Output of Sample Dataset Following is the outcome generated by the algorithm. It detects three communities for the given example which is
Figure 2. Output of Sample Dataset Following is the outcome generated by the algorithm. It detects three communities for the given example which is
as a number of communities detected by the algorithm. With this, the new Greedy incremental approach for community detection has been presented implemented and ested on various standard data sets and other random example sets. The outcome produced by the approach is the number of communities. The result is compared with the well-known results available. For the graphs which are not (00 dense, this algorithm performs nearer to the standard number of communities. From that, we can conclude that GICD cannot work well with a too much dense graph so there is a scope for improvement there. Apart from this, as the social network data is tremendously huge, one can go ahead with the parallel implementation of the same. The results are taken for the 13 different datasets which are presented in the following table. For each dataset, no
as a number of communities detected by the algorithm. With this, the new Greedy incremental approach for community detection has been presented implemented and ested on various standard data sets and other random example sets. The outcome produced by the approach is the number of communities. The result is compared with the well-known results available. For the graphs which are not (00 dense, this algorithm performs nearer to the standard number of communities. From that, we can conclude that GICD cannot work well with a too much dense graph so there is a scope for improvement there. Apart from this, as the social network data is tremendously huge, one can go ahead with the parallel implementation of the same. The results are taken for the 13 different datasets which are presented in the following table. For each dataset, no
Fig.1. Different Security Issues in the Cloud Data Storage Below listed are the main cloud security issues which are addressed by the cloud service providers while applying and delivering the cloud services to end users. There are a number of security issues related to Cloud Computing frameworks for data storage. During this section, various security challenges are discussed.
Fig.1. Different Security Issues in the Cloud Data Storage Below listed are the main cloud security issues which are addressed by the cloud service providers while applying and delivering the cloud services to end users. There are a number of security issues related to Cloud Computing frameworks for data storage. During this section, various security challenges are discussed.
“In [2], ‘using the attribute-driven mehodologythe authors presented the systematic study of security and privacy issues in cloud computing. They identified the most representative security/privacy attributes (e.g., confidentiality, integrity, availability, accountability, and privacy-preservability), as well as discussed the vulnerabilities, which may be exploited by adversaries in order to perform various attacks. Defence strategies and suggestions | were discussed as well. afc cots ateilie
“In [2], ‘using the attribute-driven mehodologythe authors presented the systematic study of security and privacy issues in cloud computing. They identified the most representative security/privacy attributes (e.g., confidentiality, integrity, availability, accountability, and privacy-preservability), as well as discussed the vulnerabilities, which may be exploited by adversaries in order to perform various attacks. Defence strategies and suggestions | were discussed as well. afc cots ateilie
Based on the study that was conducted on the above mentioned different methods in this paper, an analysis of all these methods was presented below to point out the current research gaps. Table 1 is showing the comparative study of several recent methods with respect to the different security requirements. a NN ON a Ne a eee
Based on the study that was conducted on the above mentioned different methods in this paper, an analysis of all these methods was presented below to point out the current research gaps. Table 1 is showing the comparative study of several recent methods with respect to the different security requirements. a NN ON a Ne a eee
In [11], this introduced the key management approach based on a session key convergent called SKC in order to secure the dynamic update during the process of data deduplication. Using this approach,, every data owner verified the session key correctness as well as changes it dynamically with the data update. They conducted the security analysis in order to demonstrate both SKC and CKS (convergent key sharing) methods in terms of their protection for the confidentiality of the data and the convergent key in the case se of the dynamic updates. a | ne eT. 7. in. e
In [11], this introduced the key management approach based on a session key convergent called SKC in order to secure the dynamic update during the process of data deduplication. Using this approach,, every data owner verified the session key correctness as well as changes it dynamically with the data update. They conducted the security analysis in order to demonstrate both SKC and CKS (convergent key sharing) methods in terms of their protection for the confidentiality of the data and the convergent key in the case se of the dynamic updates. a | ne eT. 7. in. e
PA: Public Auditing, PP: Privacy Preserving, SDS: Secure Data Sharing, SE: Scalabilit Evaluation, TP: Third Party, TPS: Third Party Scalability
PA: Public Auditing, PP: Privacy Preserving, SDS: Secure Data Sharing, SE: Scalabilit Evaluation, TP: Third Party, TPS: Third Party Scalability
diagram is described in figure | and 1 (a). The research methodology considers the Citrus fruit classification process of Image preprocessing and Stem removal process is derived in this part. We introduce a tractable citrus fruit (Orange) stem removal process of morphological open image, distance, top-hat filtering and thresholding which is a natural extension of image stem removal process. The stem removal preprocessing considers phase I & II process flow diagram is described in figure 1 and 1 (a). Fig. 1: Proposed Stem Removal Flow Diagram
diagram is described in figure | and 1 (a). The research methodology considers the Citrus fruit classification process of Image preprocessing and Stem removal process is derived in this part. We introduce a tractable citrus fruit (Orange) stem removal process of morphological open image, distance, top-hat filtering and thresholding which is a natural extension of image stem removal process. The stem removal preprocessing considers phase I & II process flow diagram is described in figure 1 and 1 (a). Fig. 1: Proposed Stem Removal Flow Diagram
original image with stem and removal stem image. Fig. 2: Original Citrus Image structuring element object, not an array containing multiple
original image with stem and removal stem image. Fig. 2: Original Citrus Image structuring element object, not an array containing multiple
Fig. 4: Stem Removal Results
Fig. 4: Stem Removal Results
Fig. 5: Final Stem Removal Image Preprocessed Results figure 5 shows the Final Preprocessed train images. The Fourth stage, Stem removal process is to be estimate with help of Finest StemcompleteRm thresholding. In Top-hat filtering results, the grey threshold method is to find the appropriate value of stem portion. So we can calculate the absolute difference with the original V-portion and threshold part is called preprocessed stem removal image result. The ficure 5 shows the Final Preprocessed train images. part is called preprocessed stem removal image result. The
Fig. 5: Final Stem Removal Image Preprocessed Results figure 5 shows the Final Preprocessed train images. The Fourth stage, Stem removal process is to be estimate with help of Finest StemcompleteRm thresholding. In Top-hat filtering results, the grey threshold method is to find the appropriate value of stem portion. So we can calculate the absolute difference with the original V-portion and threshold part is called preprocessed stem removal image result. The ficure 5 shows the Final Preprocessed train images. part is called preprocessed stem removal image result. The
FIGURE 1. PLOT OF IRIS DATASET USING ORIGINAL K-MEANS
FIGURE 1. PLOT OF IRIS DATASET USING ORIGINAL K-MEANS
TABLE 5, PERFORMANCE COMPARISON TABLE One of the problems in k-means clustering is random selection of initial centroids. Due to random selection of initial centroids, the clustering changes from one run to another as it is dependent on the initial centroids. In this work, we have proposed a better way of calculating initial centroids for k-means clustering. The proposed algorithm suggested in the paper helps to keep clustering stable as the initial centroids are calculated rather than random selection. The experimental results showed that the proposed k-means algorithm worked well in terms of accuracy. The clustering was the same irrespective of several runs of the proposed k-means whereas it is not the case with tradition k-means.
TABLE 5, PERFORMANCE COMPARISON TABLE One of the problems in k-means clustering is random selection of initial centroids. Due to random selection of initial centroids, the clustering changes from one run to another as it is dependent on the initial centroids. In this work, we have proposed a better way of calculating initial centroids for k-means clustering. The proposed algorithm suggested in the paper helps to keep clustering stable as the initial centroids are calculated rather than random selection. The experimental results showed that the proposed k-means algorithm worked well in terms of accuracy. The clustering was the same irrespective of several runs of the proposed k-means whereas it is not the case with tradition k-means.
clustering of data is over and if the new instance comes in, instead of redoing the entire clustering again, the new instance can be fit in the appropriate cluster with its attribute values. As can be seen, the iris dataset required a minimum of 2 iterations to implement the original K-Means whereas it converged in the first iteration itself in the proposed k-means clustering algorithm. Figure 2 shows the clustering plot for iris dataset using proposed k-means. Table 1 shows the actual cluster distribution in different datasets.
clustering of data is over and if the new instance comes in, instead of redoing the entire clustering again, the new instance can be fit in the appropriate cluster with its attribute values. As can be seen, the iris dataset required a minimum of 2 iterations to implement the original K-Means whereas it converged in the first iteration itself in the proposed k-means clustering algorithm. Figure 2 shows the clustering plot for iris dataset using proposed k-means. Table 1 shows the actual cluster distribution in different datasets.
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