Attack and anomaly detection in IoT Networks using machine learning approaches

Abstract

The rapid expansion of the Internet of Things (IoT) gives Intruders a wide attack surface from which they can conduct more damaging cyber-attacks. Scan, Spying, Denial of Service, Data Type Probing, Malicious Control, and Malicious Operation are such attacks and anomalies that can bring down an IoT system, which makes Attack and anomaly detection in IoT a rising concern and creating a powerful Intrusion Detection System (IDS) a primary need. The main goal of an intrusion detection system (IDS) is detecting attacks and any attempt to break down networks. Machine learning techniques have recently been used in intrusion detection systems since they have shown the ability to learn and adapt, besides providing a quick response. This work proposes an intrusion detection framework that can classify network activities as “Normal” or “Attack” using various machine learning methods. A common dataset called BoT-IoT evaluated the suggested model using KNIME analytics Platform.