Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks
It addresses security vulnerabilities in IoT networks for cybersecurity applications, but is incremental as it applies existing methods to a specific domain.
This paper tackled botnet attack detection in Edge-Computing-Assisted IoT networks by comparing Random Forest, XGBoost, and LightGBM models on a recognized dataset, achieving results that demonstrate their potential for practical deployment in resource-constrained environments.
The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, as attackers can compromise entire networks by targeting a single IoT device. In light of escalating cybersecurity threats, particularly botnet attacks, this paper investigates the application of machine learning techniques to enhance security in Edge-Computing-Assisted IoT environments. Specifically, it presents a comparative analysis of Random Forest, XGBoost, and LightGBM -- three advanced ensemble learning algorithms -- to address the dynamic and complex nature of botnet threats. Utilizing a widely recognized IoT network traffic dataset comprising benign and malicious instances, the models were trained, tested, and evaluated for their accuracy in detecting and classifying botnet activities. Furthermore, the study explores the feasibility of deploying these models in resource-constrained edge and IoT devices, demonstrating their practical applicability in real-world scenarios. The results highlight the potential of machine learning to fortify IoT networks against emerging cybersecurity challenges.