UIDS: a unified intrusion detection system for IoT environment

Intrusion detection system (IDS) using machine learning approach is getting popularity as it has an advantage of getting updated by itself to defend against any new type of attack. Another emerging technology, called internet of things (IoT) is taking the responsibility to make automated system by communicating the devices without human intervention. In IoT based systems, the wireless communication between several devices through the internet causes vulnerability for different security threats. This paper proposes a novel unified intrusion detection system for IoT environment (UIDS) to defend the network from four types of attacks such as: exploit, DoS, probe, and generic. The system is also able to detect normal category of network traffic. Most of the related works on IDS are based on KDD99 or NSL-KDD 99 data sets which are unable to detect new type of attacks. In this paper, UNSW-NB15 data set is considered as the benchmark dataset to design UIDS for detecting malicious activities in the network. The performance analysis proves that the attack detection rate of the proposed model is higher compared to two existing approaches ENADS and DENDRON which also worked on UNSW-NB15 data set.