Matrix Profile Based Algorithms using Self-Collected Data for Detecting DDoS Attacks in IoT Equipment

With the advent of emerging mobile network technologies such as 5G and Beyond 5G, the proliferation of Internet of Things (IoT) or, more broadly, Internet of Everything (IoE) integration has become ubiquitous across various industries and daily routines. Nevertheless, the pervasive vulnerabilities inherent in IoT networks-stemming from their widespread distribution, coupled with the limited security measures and processing capabilities of IoT devices-render them susceptible to a myriad of threats, notably Distributed Denial of Service (DDoS) attacks, which pose a grave risk to network availability. In response to the imperative for lightweight DDoS detection in IoT environments with resource constraints, this study is centered on Matrix Profile (MP)-based anomaly detection. Renowned for its effectiveness in analyzing time series data and distinguished by its expedited processing and minimal computational burden, this research undertakes a comparative analysis of six MP-based algorithms. Four unsupervised and two supervised algorithms are analyzed. These algorithms are specifically tailored to operate efficiently on IoT devices. The overarching objective is to assess the efficacy of these algorithms in identifying DDoS attacks through the utilization of system data derived from IoT devices. The study also endeavors to propose a novel approach aimed at fortifying the security posture of IoT networks against the pervasive threat of DDoS attacks.