Papers by Shorouq AL-eidi
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Code readability and software complexity are considered essential components of software quality.... more Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% ef...
Covert Timing Channels Detection Based on Image Processing Using Deep Learning
Time-Ordered Bipartite Graph for Spatio-Temporal Social Network Analysis
2020 International Conference on Computing, Networking and Communications (ICNC)
Today, human beings are surrounded by heterogeneous networking environments consisting of growing... more Today, human beings are surrounded by heterogeneous networking environments consisting of growing numbers of portable computation and communication devices. As most devices are carried by people, this creates communication networks between them that are highly influenced by human mobility. This implies that the presence of possible patterns in human movements can be exploited by network applications in order to extract valuable information that can be used to develop new services. Extracting this information does not concern only the spatial or temporal dimensions, but also the social aspects of the people involved. To evaluate this information, and capture the important and relevant features regarding both spatial and temporal dimensions, we have developed a model that is sufficiently expressive and easily tunable. Using complex network theory to analyze user mobility patterns in spatio-temporal social networks has produced a number of significant results in previous studies. However, these studies have often neglected time properties in spatio-temporal social networks and used static models to represent them. However, in real-world scenarios, the time effects play a pivotal role. In this study, we propose a new model to analyze spatio-temporal social networks represented by bipartite graphs, and extend the static centrality to temporal centrality, with a focus on closeness and betweenness. Our results show that the time-ordered bipartite graph provides a dynamic and comprehensive analysis of real spatio-temporal social networks, and that temporal centrality better captures the effects of time on the topology of networks over static models.
Optimal Power Consumption in Cooperative WSNs for a Random Distance using 2-D Topology
Wireless sensor networks a combination of advance made in the field of analog and digital circuit... more Wireless sensor networks a combination of advance made in the field of analog and digital circuits. A wireless sensor network usually consists of self-organized and installed devices called sensor. These sensors are used to sense, collect and send information in remotely areas, the sensors are deployed and they are able to be installed, organized, and cooperate with each other. However, the sensors are supplied by small batteries, which is difficult to be recharged and replaced in difficult environments, such as natural habitats. Therefore, power consumption is one of the main issues considered in this field. Studies discussed the optimal power consumption in fixed distance, while the random distribution is not considered. This study discusses the optimal power consumption in such grids topology with random distance and variance number of nodes in this topology. The study assumes that the grid consists of 2*2, up to 6*6 nodes. The results show that the diagonal path between the send...
IEEE Access, 2021
With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (... more With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve. INDEX TERMS Covert timing channels, detection, entropy, image processing, machine learning.
Road Importance Using Complex-Networks, Graph Reduction & Interpolation
2020 International Conference on Computing, Networking and Communications (ICNC), 2020
Most people spend hours on the road on a daily basis making road networks a crucial part of our d... more Most people spend hours on the road on a daily basis making road networks a crucial part of our daily lives. Trips to work, grocery store, hospital or even casual jogs and road trips mainly occur on walkable or drivable roads. With the increase of online communities, professionals and enthusiasts, road networks are now abundantly available from various sources making them a great resource for a variety of analysis such as finding the road importance, road characteristics, city planning, and the association between neighborhoods’ walkability and the local obesity rate. However, as data increases, analyzing larger regions requires much more processing power and computational time. We aim to incorporate graph reduction and centrality interpolation while utilizing some already-efficient complex networks centrality algorithms, to produce ready-to-analyze road scores for the entire given data set while reducing the required computational time when compared to the conventional algorithms that do not use reduction. Furthermore, our produced road scores can be applied to non-network characteristics such as amenities, elevation, road type, road condition and road structure to produce more accurate walkability scores.
Sensors, 2020
Covert timing channels are an important alternative for transmitting information in the world of ... more Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legiti...
Convolutional Neural Network Structure to Detect and Localize CTC Using Image Processing
2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)
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Papers by Shorouq AL-eidi