Technology advancement has not only made the world a global village but also made it possible for people to interact with devices around them through the Internet of Things (IoT) and bring your own devices (BYOD) technology. Organizations have been caught up in this rush as many employees in corporations and students in learning institutions bring different devices (such as smartphones, smart watches, smart screens, tablets, smart wrist bands that measure activity of one's organs) with them and connect to the corporate networks. IoT/BYOD has greatly improved efficiency and has led to a happy work force since they can now perform their duties efficiently as much of activities orbiting around their social life are within grasp. However, the downside of this increase in connected mobile devices is a cocktail of security threats that makes cyber security management critical for business continuity. In this paper, we propose an enhanced security scheme in IoT by use of social network analysis to single out central nodes that have the greatest influence in attack propagation. Simulation results show that the proposed method can be used to deactivate the influential node in the network in case of an attack thereby reducing the catastrophic impact. The significance of our enhanced IoT security approach is its ability to detect nodes with suspicious activities and thereby take them off the grid.
Published In:IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 192-199
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Charles O. Muango :
Ph.D. Candidate, MSc.in Data Communication, BSc. In Computer Science. Currently am Employed at Masinde Muliro University of Science and Technology in the School of Computer and Informatics. Current research interests-Network Security, Cyber-Security, Social Networks, Deep learning & Human Computer Interaction.
Jairus O. Odawa :
Ph.D. Candidate, MSc. IT, BSc. in Computer Science. Assistant Lecturer at Masinde Muliro University of Science and Technology in the School of Computer and Informatics.
Laban O. Oenga :
Ph.D. Candidate, MSc. IT, BSc. in Computer Science. Assistant Lecturer at Masinde Muliro University of Science and Technology in the School of Computer and Informatics.
Qu Shaojian :
(Ph.D.) Prof at the University of Shanghai for Science and Technology in the School of Business Management.
The aim of this paper was to use metrics to be able to identify a central node that is more active than the rest in the event of an attack then isolate it from the network. This was done by comparing values of the centrality measures from the simulation of our network. This means that when you have a network it is possible to get the epidemic threshold and the central nodes involved in malware spread through calculation and thereby being able to mitigate damage.
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