A model for intrusion detection system using hidden Markov and variational Bayesian model for IoT based wireless sensor network

Abstract

Significant growth of smart home devices is adopted, which provides security, convenience, and energy efficiency for users, in recent years. As an instance, consider a secured smart camera that detects movements of unauthorized objects, whereas the fire accidents can be detected by smoke sensors. Though, a surface for new cyber threats is some of the most recent examples that are open up in this field. Furthermore, recent examples also show that the distributed denial of service (DDoS) attacks are performed by misusing the smart devices which are hacked violating the privacy rules. In this proposed work, the application of machine learning in an environment of smart homes is explored so that the anomalous activities that occur can be identified. The sensor data at the network level is trained using a model based on the Markov chain known as hidden Markov model (HMM) and is created using smart devices and multiple sensors from a testbed. The model generated using HMM achieves 97% accuracy while detecting potential anomalies where attacks are indicated. In this approach, we construct the model and differentiate the analysed results with existing techniques. The starting step for securing IoT networks is intrusion detection and the next step is the intruders prediction which provides an active defence against the incoming attacks. The model employs an algorithm that does not depend on specific domain knowledge. The work has achieved an improvement in prediction accuracy of 5% for an alert category over the current variable length methods of Markov chain intrusion prediction, as they provided information more for a possible defence. The DDoS attack is considered as a coordinated attack mainly carried out based on a large scale depending on whether the target system’s resources or services are available. Thus, the novel approach also describes a novel machine learning-based technique using an algorithm known as variational dynamic Bayesian algorithm which helps to obtain a HMM with the number of parameters and model states which are optimized for the prediction of a DDoS attack. This procedure conquers the speed of a moderate combination HMM approach.