Detection of IoT-botnet attacks using fuzzy rule interpolation

Recently, the Internet of Things (IoT) has been used in technology for different aspects to increase the efficiency and comfort of human life. Protecting the IoT infrastructure is not a straightforward task. There is an urgent need to handle different attack scenarios within the IoT smart environment. Attackers continuously targeted the modern aspects of technology, and trying abusing these technologies using complex attack scenarios such as Botnet attacks. Botnet attacks considered a serious challenge faces of the IoT smart environment. In this paper, we introduce a novel idea that capable of supporting the detecting of IoT-Botnet attack and in meanwhile to avoid the issues associated with the deficiencies of the knowledge-based representation and the binary decision. This paper aims to introduce a detection approach for the IoT-BotNet attack by using the Fuzzy Rule Interpolation (FRI). The FRI reasoning methods added a benefit to enhance the robustness of fuzzy systems and effectively reduce the system’s complexity. These benefits help the Intrusion Detection System (IDS) to generate more realistic and comprehensive alerts. The proposed approach was applied to an open-source BoT-IoT dataset from the Cyber Range Lab of the center of UNSW Canberra Cyber. The proposed approach was tested, evaluated and obtained a 95.4% detection rate. Moreover, it effectively smooth the boundary between normal and IoT-BotNet traffics because of its fuzzy-nature, as well as, it had the ability to generate the required IDS alert in case of the deficiencies of the knowledge-based representation.

@article{al2020detection,
  title={Detection of IoT-botnet attacks using fuzzy rule interpolation},
  author={Al-Kasassbeh, Mouhammd and Almseidin, Mohammad and Alrfou, Khaled and Kovacs, Szilveszter},
  journal={Journal of Intelligent & Fuzzy Systems},
  volume={39},
  number={1},
  pages={421--431},
  year={2020},
  publisher={IOS Press}
}