Hybrid intrusion detection system for wireless IoT networks using deep learning algorithm

Abstract

The intense growth of wireless communication and the digital revolution have increased the utilization of Internet of Things (IoT) applications. The number of internet users increase everyday which in turn increases the network traffic and data volume. The energy-limited sensor node resources in the IoT environment are vulnerable to attacks due to the networking procedures, open broadcast communication, etc. Intruders easily gain access to the network and perform different types of attacks which degrades the overall performance and quality of services. Intrusion detection systems are developed to detect different kinds of attacks that cannot be detected by the firewalls. Based on the features, the intrusion detection system classifies the normal and abnormal characteristics of the system. Various intrusion detection systems based on machine learning models have evolved so far. However, the feature selection process is much important to enhance the classification performance. Thus, in this research work, a deep learning-based feature selection procedure is presented to select the optimal features. The decision tree algorithm is utilized as a classifier to classify the deep features and detect attacks in the IoT network. Benchmark NSL-KDD dataset has been used for experimentation and the parameters like precision, recall, f1-score, and accuracy are evaluated for the proposed model and the conventional models to validate the superior performance. With maximum accuracy of 99.49%, the proposed hybrid model performs better than the conventional intrusion detection systems.