Evaluation of contemporary intrusion detection systems for internet of things environment

Vandana Choudhary, Sarvesh Tanwar, Tanupriya Choudhury

Abstract: Internet of Things (IoT) involves wide-ranging devices connected through the Internet with an aim to enable coherent communication amongst them without human intervention to realize profuse smart applications which inherently makes our life a lot easier and furthermore productive. These connected devices continuously sense and gather information from surroundings, thereby producing an immense amount of data that cater for big data analytics. In the current era, number of smart devices are increasing rapidly due to the magnificent features they offer. Moreover, public access to the Internet makes the system even more vulnerable to intrusions. Catastrophically, this has fascinated numerous cybercriminals who have turned the IoT ecosystem into a hotbed of illicit activities. Thereupon, implication of Intrusion Detection System (IDS) in IoT is apparent. The literature suggests a number of IDS to address intrusions/attacks in the discipline of IoT. In the current paper, besides Systematic Literature Review of the IDS for IoT environment, a deep learning model with aquila optimization is proposed to predict anomaly using IoTID20, UNSW-NB15–1 and UNSW_2018_IoT_Botnet_Full5pc_4 datasets. The hybrid model that we have developed, uses a combined network structure of convolutional neural network and aquila optimization algorithm. In all of the studies that were carried out, the swarm intelligence-driven deep learning strategy outperformed other, comparable approaches. Based on current findings, it is reasonable to draw the conclusion that the suggested technique offers an efficient method for early anomaly detection and contributes to viable control of anomaly in the IoT environment.