Comparing matrix profile lightweight algorithms usingself-collected data for detecting DDoS attacks in IoTequipment

With the rise of mobile network technologies like 5G and Beyond 5G, the integration of Internet of Things (IoT) or, more broadly, Internet of Everything (IoE) has become increasingly widespread across industries and daily life. However,the inherent vulnerabilities in IoT networks, characterized by their extensive distribution and the limited security measures and processing power of IoT devices, expose them to various threats, notably Distributed Denial of Service (DDoS) attacks that jeopardize network availability. To address the challenge of lightweight DDoS detection in IoT environments with constrained resources, this study focuses on Matrix Profile (MP) based anomaly detection. Known for its efficacy in analyzing time series data and its advantages in terms of speed and minimal processing load, six MP based algorithms are compared in this research, all designed to operate efficiently on IoT devices. The goal is to evaluate the performance of these algorithms in detecting DDoS attacks using the system data of IoT devices, proposing a new approach to enhance the security of IoT networks against these threats.