Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

TL;DR

This paper explores the use of advanced machine learning algorithms to detect and classify botnet attacks in edge-assisted IoT networks, aiming to improve security in resource-constrained environments.

Contribution

It provides a comparative analysis of ensemble learning models and assesses their practical deployment in IoT edge devices for botnet detection.

Findings

Random Forest, XGBoost, and LightGBM achieve high detection accuracy.

Models are feasible for deployment on resource-constrained IoT devices.

Machine learning enhances IoT network security against botnet threats.

Abstract

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.