IoT based Intelligent Intrusion Detection System in Smart Environment

Abstract

In this research, an efficient Intrusion Detection Systems (IDS) for IoT networks integrated with Wireless Sensor Actuator Networks, through hybrid model techniques is designed. The IoT based networks consist of highly constrained devices able to communicate wirelessly, thus following the architecture of Wireless Sensor networks. Current state of the art of IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they work more efficiently than that addresses to the paradigm of ad-hoc networks, thus are highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. newlineIn this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. The existing Intrusion Detection Systems which are mostly designed for detecting the particular form of Intrusion for Wireless Sensor Network (WSN) has many restrictions for different types of attacks and network structures. A novel strategy for intrusion detection based on knowledge has to be applied where the attacks are prevented from creating deviation of normal features and also from various other aggregated shapes. Thus, as samples are traced in different stages, the threats need to be averted, with many solutions possible, and one of the best solution possible is to design a model of Intrusion Detection System (IDS), with the approach of Bayesian and Hidden Markov Network. In the proposed framework, the IDS are designed with different processing levels of training and testing based on connection records. newline newline