Research Area:  Machine Learning

Abstract:

IoT (Internet of Things) systems are still facing a great number of attacks due to their integration in several areas of life. The most-reported attacks against IoT systems are "Denial of Service" (DoS) and "Distributed Denial of Service" (DDoS) attacks. In this paper, we investigate DoS/DDoS attacks detection for IoT using machine learning techniques. We propose a new architecture composed of two components: DoS/DDoS detection and DoS/DDoS mitigation. The detection component provides fine-granularity detection, as it identifies the specific type of attack, and the packet type used in the attack. In this way, it is possible to apply the corresponding mitigation countermeasure on specific packet types. The proposed DoS/DDoS detection component is a multi-class classifier that adopts the "Looking-Back" concept, and is evaluated on the Bot-IoT dataset. Evaluation results show promising results as a Looking-Back-enabled Random Forest classifier achieves an accuracy of 99.81%.

Keywords:  
Denial Of Service Attack Detection
Mitigation
Internet Of Things
Machine Learning
Deep Learning

Author(s) Name:  

Publisher name:  ELSEVIER

DOI:  https://doi.org/10.1016/j.compeleceng.2022.107716

Volume Information:  Volume 98, March 2022, 107716

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0045790622000337