Cognitively-inspired Agent-based Service Composition for Mobile & Pervasive Computing

Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. Our approach exhibits features such as distributedness, modularity, emergent global functionality, and robustness, which endow it with capabilities to perform decentralized service composition by orchestrating manifold service providers and conflicting goals from multiple users. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.Comment: This paper will appear on AIMS'19 (International Conference on Artificial Intelligence and Mobile Services) on June 2

Anomaly intrusion detection using machine learning- IG-R based on NSL-KDD dataset

Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process

Use of social networking in the Middle East: student perspectives in higher education

This study aims to determine the benefits, risks, awareness, cultural factors, and sustainability, allied to social networking (SN) use in the higher education (HE) sector in Middle Eastern countries, namely Jordan, Saudi Arabia, and Turkey. Using an online survey, 1180 complete responses were collected and analyzed using the statistical confirmatory factor analysis method. The use of SN in the Middle Eastern HE sector has the capacity to promote and motivate students to acquire professional and personal skills for their studies and future workplace; however, the use of SN by tertiary students is also associated with several risks: isolation, depression, privacy, and security. Furthermore, culture is influenced by using SN use, since some countries shifted from one dimension to another based on Hofstede's cultural framework. The study new findings are based on a sample at a specific point in time within a culture. The study findings encourage academics to include SN in unit activities and assessments to reap the benefits of SN, while taking steps to mitigate any risks that SN poses to students. Although other studies in the Middle East examined the use of Learning Management System and Facebook in, HE as a means of engaging students in discussions and communications, however, this study contributes a better understanding of the benefits and risks, awareness, culture, and sustainability, associated with the use of SN in the HE sector in the Middle East. Finally, the paper concludes with an acknowledgment of the study limitations and suggestions for future research

Autonomous management for service specific overlay networks

Overlay networks emerging as a main player in content delivery because they provide effective and reliable services that are not otherwise available. Extensive research has recently focused on the design of Service Specific Overlay Networks (SSON) to deliver media in a heterogeneous environment. This dissertation investigates the problem of SSON's management, and proposes an autonomous SSON management framework. The framework consists of a policy layer that in turn constitutes a set of Overlay Policy Enforcement Points (OPEP) and Overlay Policy Decision Points (OPDP). An OPEP is where policy decisions are actually enforced---policy decisions are made primarily at the OPDP. The research plan presented in this dissertation addresses the functionalities of these components. To realize dynamic SSONs construction, a novel, fault-resilient semantic overlay for MediaPorts resource discovery is proposed. It allows services to be efficiently and accurately located, and is based on a widely studied family of chordal rings called the optimal chordal ring. In addition to the semantics of the services offered, our solution is based on the geographical locations of the nodes. The increased complexity and heterogeneity of SSONs led to the proposal of autonomic overlays management architecture. Overlays are viewed as a dynamic organization for self-management in which self-interested nodes can join or leave according to their specific goals. It dynamically adapts the behavior of the overlay network to the preferences of the user, network, and service providers. To capture the overlay nodes autonomic behavior, a new approach for SSONs self-organized composition is proposed. Using a self-organizing approach, autonomic entities are dynamically and seamlessly composed into SSONs to achieve system-wide goals. The algorithm that encompasses that approach is powered by learning rules induced from biological systems, and endowed with filtering rules to achieve the highest possible performance. Experimental studies are presented to demonstrate the performance of the proposed schemes