Evaluation of Intrusion Detection System for the Distributed Denial of Service Attack on Internet of Things in Fog Computing Environment
Miyelani Silence Madiba, Mthulisi Velempini · 2023
The Internet of Things (IoT) architecture is susceptible to Distributed Denial of Service (DDoS) attacks and can be used as a launchpad for other DDoS-related attacks. The volume of DDoS attacks is continuously growing. The main goal of a DDoS attack is to deny legitimate users network resources such as bandwidth. The attacker focuses on non-legacy IoT devices since they have weak built-in defense and other drawbacks like limited processing power and power consumption. In terms of security considerations, DDoS attacks are a well-known challenge in a fog computing environment. The IoT is posing various security issues as it is applied in every area of life, such as body area networks, home area networks, and services-related networks. The number of linked devices, which is always growing, increases the possibility of cyberattacks. Hence, an effective and efficient Intrusion Detection System (IDS) is required which is highly scalable and dynamic. The study evaluates the performance of five IDS techniques in MATLAB using four metrics. The results show that anomaly-based IDS, deep learning-based IDS and machine learning-based IDS are promising approaches.