Efficient Intrusion Detection System for IoT Environment

Abstract

These days, the Internet is subjected to a variety of attacks that can harm network devices or allow attackers to steal the most sensitive data from these devices. IoT environment provides new perspective and requirements for Intrusion detection due to its heterogeneity. This paper proposes a newly developed Intrusion Detection System (IDS) that relies on machine learning and deep learning techniques to identify new attacks that existed systems fail to detect in such an IoT environment. The paper experiments consider the benchmark dataset ToN_IoT that includes IoT services telemetry, Windows, Linux operating system, and network traffic. Feature selection is an important process that plays a key role in building an efficient IDS. A new feature selection module has been introduced to the IDS; it is based on the ReliefF algorithm which outputs the most essential features. These extracted features are fed into some selected machine learning and deep learning models. The proposed ReliefF-based IDSs are compared to the existed IDSs based correlation function. The proposed ReliefF-based IDSs model outperforms the previous IDSs based correlation function models. The Medium Neural Network model, Weighted KNN model, and Fine Gaussian SVM model have an accuracy of 98.39 %, 98.22 %, and 97.97 %, respectively.