A Robust Security Framework for Combating Malicious Attacks for Internet of Things

Abstract

Extensive adoption of intelligent devices with ubiquitous connectivity has increased Internet of Things (IoT) traffic tremendously. The smart devices promise to improve human life through improved safety and security through the implementation of intelligent transportation systems, optimization of power grids,and applications in human health. Devices produce a large amount of data for analytic applications running inside a cloud infrastructure. Unlike core networks, the main objective of an attack on an IoT network is to disrupt the availability of IoT data for the applications by overwhelming devices with information requests. Detection of such an attack cannot be done either in the cloud where the analytical application runs nor on the IoT device itself due to its limited computational resources. Furthermore, the standard networking paradigm does not provide an easy way to instrument and control networking nodes for an effective mitigation of identified threats. In this work, we propose a fogassisted software defined networking (SDN) driven intrusion deteciv v tion/prevention system (IDPS) for IoT networks. A collocated fog computational arrangement with IoT network equips proposed IDPS for timely identification of various attack models in near real time for effective neutralization of threats using SDN control. We have evaluated our proposed scheme using machine learning and controller performance metrics i.e. accuracy, precision, f1 score , successful and unsuccessful detection of possible attacks, packet loss and throughput etc. We have also evaluated our scheme on renowned network traces to prove the idea. We have found our approach more effective in contrast to traditional techniques for intrusion detection in the IoT network. Moreover, we have presented and evaluated a scheme for detection of botnets in IoT network, keeping in view the other very important aspect of DDoS attack detection and prevention.