Sensors, Vol. 24, Pages 2626: RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Sensors, Vol. 24, Pages 2626: RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic Sensors doi: 10.3390/s24082626 Authors: Sarah Alkadi Saad Al-Ahmadi Mohamed Maher Ben Ismail Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research