Effective injection of adversarial botnet attacks in IoT ecosystem using evolutionary computing

Abstract

With the widespread adoption of Internet of Things (IoT) technologies, botnet attacks have become the most prevalent cyberattack. In order to combat botnet attacks, there has been a considerable amount of research on botnet attacks in IoT ecosystems by graph-based machine learning (GML). The majority of GML models are vulnerable to adversarial attacks (ADAs). These ADAs were created to assess the robustness of existing ML-based security solutions. In this letter, we present a novel adversarial botnet attack (ADBA) that modifies the graph data structure using genetic algorithms (GAs) to trick the graph-based botnet attack detection system. According to the experiment results and comparative analysis, the proposed ADBA can be executed on resource-constrained IoT nodes. It offers a substantial performance gain of 2.15 s, 52 kb, 92 817 mJ, 97.8%, and 27.74%–41.82% over other approaches in term of Computing Time (CT), Memory Usage (MU), Energy Usage (EU), Attack Success Rate (ASR) and Accuracy (ACC) metrics, respectively.