Malicious Insider Attack Detection in IoTs Using Data Analytics

Abstract

Internet of Things (IoTs) are set to revolutionize our lives and are widely being adopted nowadays. The IoT devices have a range of applications including smart homes, smart industrial networks and healthcare. Since these devices are responsible for generating and handling large amounts of sensitive data, the security of the IoT devices always poses a challenge. It is observed that a security breach could effect individuals and eventually the world at large. Artificial intelligence (AI), on the other hand, has found many applications and is widely being explored in providing security specifically for IoT devices. Malicious insider attack is the biggest security challenge associated with the IoT devices. Although, most of the research in IoT security has pondered on the means of preventing illegal and unauthorized access to systems and information; unfortunately, the most destructive malicious insider attacks that are usually a consequence of internal exploitation within an IoT network remains unaddressed. Therefore, the focus of this research is to detect malicious insider attacks in the IoT environment using AI. This research presents a lightweight approach for detecting insider attacks and has the capability of detecting anomalies originating from incoming data sensors in resource constrained IoT environments. The results and comparison show that the proposed approach achieves better accuracy as compared to the state of the art in terms of: a) improved attack detection accuracy; b) minimizing false positives; and c) reducing the computational overhead.