Biologically-inspired SDN-based Intrusion Detection and Prevention Mechanism for Heterogeneous IoT Networks

Currently, technology revolution relies on creating an intelligent environment by establishing interconnections between physical objects or “things” to communicate with each other. Latest Internet of Things (IoT) technology enables prodigious connectivity between autonomous heterogeneous devices/users over multiple access technologies. While, data transfer over such large, dynamic, heterogeneous network usually gets compromised by prying attacks. Therefore, several security approaches are used to guarantee data authentication, identify valid users (host/guest), and detect malevolent and suspicious behavior. In this paper, we employed biologically-inspired Genetic Algorithm (GA) to be part of elastic Software Defined Network (SDN) as controller application to detect suspicious traffic and react either by blockage or by redirection to a honeypot. Simulations showed the efficiency of the presented approach in detecting different types of attacks.