Academia.eduAcademia.edu

Figure 2 – uploaded by Shafi'i Muhammad ABDULHAMID

See full PDFdownloadDownload figure
Publicly available email spam corpus. Table 2 on the detection of spam messages solely. In a real world environment where there is nothing like zero probability of wrongly categorizing < ham message, it is required that a compromise be reached between the two kinds of errors, depending on the predisposition of user and the performance indicators used. The formulae for calculating the classifi cation accuracy and classification error are depicted in Eqs. (1) and (2. below: Spam filters with a drastically reduced FPR and FNR are said to have a better performance. These standard characteristics (FNR and FPR) rep- resents the efficiency of filters that directly aim at the classification de- cision borderline devoid of generating the probability estimate. On the other hand, the efficiency of filters that explicitly estimate the group conditional probabilities and then execute classification based on esti- mated probabilities can be represented by a curve called ROC (Receiver Operating Characteristics) curve. ROC curve, is a graphical plot that demonstrates the analytical capability of a spam filter as its bias level is modified [48]. The ROC curve is generated by plotting the true positive rate (TPR) against the false positive rate (FPR) at different threshold settings [49]. The true positive rate is referred to as sensitivity, recall or probability of detection [49] in machine learning. The false-positive rate is referred to as the squabble or likelihood of false alarm. This is computed by subtracting the value of the specificity from 1 (ie. 1 - specificity). ROC testing are an outstanding standard of performance measure in spam filtering [48]. When the ROC curve of a spam filter closely sits on top of another, such filter can be classified a filter with superior performance in all implementation setups [20]. The two metrics imported from the field of information retrieval ‘recall’ and ‘precision’ are respectively utilised for obtaining the efficiency and characteristic of spam filters [50].

Table 2 Publicly available email spam corpus. Table 2 on the detection of spam messages solely. In a real world environment where there is nothing like zero probability of wrongly categorizing < ham message, it is required that a compromise be reached between the two kinds of errors, depending on the predisposition of user and the performance indicators used. The formulae for calculating the classifi cation accuracy and classification error are depicted in Eqs. (1) and (2. below: Spam filters with a drastically reduced FPR and FNR are said to have a better performance. These standard characteristics (FNR and FPR) rep- resents the efficiency of filters that directly aim at the classification de- cision borderline devoid of generating the probability estimate. On the other hand, the efficiency of filters that explicitly estimate the group conditional probabilities and then execute classification based on esti- mated probabilities can be represented by a curve called ROC (Receiver Operating Characteristics) curve. ROC curve, is a graphical plot that demonstrates the analytical capability of a spam filter as its bias level is modified [48]. The ROC curve is generated by plotting the true positive rate (TPR) against the false positive rate (FPR) at different threshold settings [49]. The true positive rate is referred to as sensitivity, recall or probability of detection [49] in machine learning. The false-positive rate is referred to as the squabble or likelihood of false alarm. This is computed by subtracting the value of the specificity from 1 (ie. 1 - specificity). ROC testing are an outstanding standard of performance measure in spam filtering [48]. When the ROC curve of a spam filter closely sits on top of another, such filter can be classified a filter with superior performance in all implementation setups [20]. The two metrics imported from the field of information retrieval ‘recall’ and ‘precision’ are respectively utilised for obtaining the efficiency and characteristic of spam filters [50].

Related Figures (18)

Fig. 1. The volume of spam emails 4th quarter 2016 to 1st quarter 2018. Many researchers and academicians have proposed different email spam classification techniques which have been successfully used to classify data into groups. These methods include probabilistic, decision
Fig. 1. The volume of spam emails 4th quarter 2016 to 1st quarter 2018. Many researchers and academicians have proposed different email spam classification techniques which have been successfully used to classify data into groups. These methods include probabilistic, decision
Fig. 2. Pictorial Representation of the Structure of this paper. There is a rapid increase in the interest being shown by the global research community on email spam filtering. In this section, we presen! similar reviews that have been presented in the literature in this domain. This method is followed so as to articulate the issues that are yet to be addressed and to highlight the differences with our current review. Lueg [17] presented a brief survey to explore the gaps in whether informatior filtering and information retrieval technology can be applied to postulate Email spam detection in a logical, theoretically grounded manner, ir order to facilitate the introduction of spam filtering technique that coulc The rest of this paper is organized as follows: Section 2 gives a
Fig. 2. Pictorial Representation of the Structure of this paper. There is a rapid increase in the interest being shown by the global research community on email spam filtering. In this section, we presen! similar reviews that have been presented in the literature in this domain. This method is followed so as to articulate the issues that are yet to be addressed and to highlight the differences with our current review. Lueg [17] presented a brief survey to explore the gaps in whether informatior filtering and information retrieval technology can be applied to postulate Email spam detection in a logical, theoretically grounded manner, ir order to facilitate the introduction of spam filtering technique that coulc The rest of this paper is organized as follows: Section 2 gives a
Summary of previous reviews in email spam filtering. Table 1
Summary of previous reviews in email spam filtering. Table 1
Fig. 3. Email server spam filtering architecture.
Fig. 3. Email server spam filtering architecture.
Levels of cost sensitivity of model. Table 3
Levels of cost sensitivity of model. Table 3
Algorithm 1 kNN Algorithm for Spam Email Classification 5.2. Naive Bayes classifier In [58], the steps involved in a simple kNN algorithm for filtering spam mails is described in the algorithm below. Here Neighbours(d) return the k nearest neighbours of d, Closest (d, t) return the closest el- ements of t in d, and testClass(S) return the class label of S. A simple kNN algorithm for spam email classification is in the algorithm below:
Algorithm 1 kNN Algorithm for Spam Email Classification 5.2. Naive Bayes classifier In [58], the steps involved in a simple kNN algorithm for filtering spam mails is described in the algorithm below. Here Neighbours(d) return the k nearest neighbours of d, Closest (d, t) return the closest el- ements of t in d, and testClass(S) return the class label of S. A simple kNN algorithm for spam email classification is in the algorithm below:
Algorithm 2 Naive Bayes Classification Algorithm for Email Spam Classification The message is classified as spam if the total spamminess product S [M] is greater than the hamminess product H [M]. The above description in [63] is used in the Naive Bayes classification algorithm for email spam classification depicted below:
Algorithm 2 Naive Bayes Classification Algorithm for Email Spam Classification The message is classified as spam if the total spamminess product S [M] is greater than the hamminess product H [M]. The above description in [63] is used in the Naive Bayes classification algorithm for email spam classification depicted below:
Fig. 4. Architecture of neural network (NN) Classifier.
Fig. 4. Architecture of neural network (NN) Classifier.
Algorithm 5 Email spam classification algorithm using Rough Set
Algorithm 5 Email spam classification algorithm using Rough Set
Fig. 5. Rough Set (RS) email filtering process workflow from user mailbox.
Fig. 5. Rough Set (RS) email filtering process workflow from user mailbox.
Algorithm 5 Email spam classification algorithm using Rough Set (continued )
Algorithm 5 Email spam classification algorithm using Rough Set (continued )
Algorithm 6 Support Vector Machine (SVM) algorithm 5.7. Decision tree
Algorithm 6 Support Vector Machine (SVM) algorithm 5.7. Decision tree
Algorithm 7 Decision Tree algorithm for Spam Filtering By partitioning the email dataset in relation to least entropy, the resultant email dataset has the highest information gain and so impurity The decision tree algorithm for classifying email messages using en- tropy algorithm is presented below:
Algorithm 7 Decision Tree algorithm for Spam Filtering By partitioning the email dataset in relation to least entropy, the resultant email dataset has the highest information gain and so impurity The decision tree algorithm for classifying email messages using en- tropy algorithm is presented below:
Fig. 6. Decision Tree Algorithm for email spam filtering. (emails contain both spam and ham) of the dataset is reduced. The dataset can be tested using the decision tree algorithm after the tree is created from the training email dataset. The email dataset being tested undergo some processing in the tree using some predefined rules pending the time it will get to a leaf node. The label in the leaf node is then assigned to the tested data. Below in Fig. 6 is a theoretical tree that illustrate how the decision tree algorithm carries out its spam filtering operation. F represents the features or words in the email message. V depicts the values or word frequencies of some words contained in the email message. C depicts the labels which are either spam/ham.
Fig. 6. Decision Tree Algorithm for email spam filtering. (emails contain both spam and ham) of the dataset is reduced. The dataset can be tested using the decision tree algorithm after the tree is created from the training email dataset. The email dataset being tested undergo some processing in the tree using some predefined rules pending the time it will get to a leaf node. The label in the leaf node is then assigned to the tested data. Below in Fig. 6 is a theoretical tree that illustrate how the decision tree algorithm carries out its spam filtering operation. F represents the features or words in the email message. V depicts the values or word frequencies of some words contained in the email message. C depicts the labels which are either spam/ham.
Algorithm 8 AdaBoost Algorithm for Email Spam Classification (Adapted from [127]) centered on the theory of hybridisation of several weak hypotheses, a very good example is the AdaBoost system. The objective of boosting is to obtain a very accurate classification rule by amalgamating several weak rules or weak hypotheses each of which may be only relatively accurate. A learner is trained in every phase of the classification process, and the result of each phase is used to add credence to data for the upcoming phases [87]. AdaBoost is the most popular boosting algorithm. It was proposed by [88]. AdaBoost can produce a good output even when the performance of the weak learners are unsatisfactory. At present Boosting is now been applied in the field of classification, regression, face recog- nition and so on. Boosting algorithms that utilised confidence rated projections are being applied to solve spam filtering problem. Literature have also shown that they can produce classification results that are better than that of Bayesian and decision tree approaches [87]. AdaBoost has become a widely accepted machine learning algorithm because of its astounding performance in solving classification problems. It is believed among some statisticians that AdaBoost has some relationship with lo- gistic regression probability maximisation [89]. The widespread use of AdaBoost according to Rob Schapire is not unconnected with the ad- vantages that the approach have over some other learning algorithm. AdaBoost is fast, the algorithm is straightforward and easy to program, absence of parameter tuning (except T) makes is less cumbersome. It is adaptable and can combine well with any learning algorithm. Also, there no need of any previous knowledge about weak learner. It is verifiably efficient, provided it can always locate rough rules of thumb. The algo- rithm is very adaptable, and can be used with data that is textual, numeric or discrete in nature. It has been expanded further to learning problems that are outside binary classification. The AdaBoost algorithm for detecting spam email is show in algorithm 8 below:
Algorithm 8 AdaBoost Algorithm for Email Spam Classification (Adapted from [127]) centered on the theory of hybridisation of several weak hypotheses, a very good example is the AdaBoost system. The objective of boosting is to obtain a very accurate classification rule by amalgamating several weak rules or weak hypotheses each of which may be only relatively accurate. A learner is trained in every phase of the classification process, and the result of each phase is used to add credence to data for the upcoming phases [87]. AdaBoost is the most popular boosting algorithm. It was proposed by [88]. AdaBoost can produce a good output even when the performance of the weak learners are unsatisfactory. At present Boosting is now been applied in the field of classification, regression, face recog- nition and so on. Boosting algorithms that utilised confidence rated projections are being applied to solve spam filtering problem. Literature have also shown that they can produce classification results that are better than that of Bayesian and decision tree approaches [87]. AdaBoost has become a widely accepted machine learning algorithm because of its astounding performance in solving classification problems. It is believed among some statisticians that AdaBoost has some relationship with lo- gistic regression probability maximisation [89]. The widespread use of AdaBoost according to Rob Schapire is not unconnected with the ad- vantages that the approach have over some other learning algorithm. AdaBoost is fast, the algorithm is straightforward and easy to program, absence of parameter tuning (except T) makes is less cumbersome. It is adaptable and can combine well with any learning algorithm. Also, there no need of any previous knowledge about weak learner. It is verifiably efficient, provided it can always locate rough rules of thumb. The algo- rithm is very adaptable, and can be used with data that is textual, numeric or discrete in nature. It has been expanded further to learning problems that are outside binary classification. The AdaBoost algorithm for detecting spam email is show in algorithm 8 below:
Algorithm 9 Random Forests Algorithm for Email Classification
Algorithm 9 Random Forests Algorithm for Email Classification
Algorithm 10 Convolutional Neural Networks for Email Classification
Algorithm 10 Convolutional Neural Networks for Email Classification
Summary of published papers that attempted spam filtering using Machine Learning techniques. Table 4
Summary of published papers that attempted spam filtering using Machine Learning techniques. Table 4
Related topics:
Machine LearningEmail Spam Filter
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved