用机器学习检测边缘计算中的僵尸网络攻击，提升物联网安全。
Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks
- 对比随机森林、XGBoost和LightGBM三种集成学习模型。
- 在真实流量数据集上实现高精度检测，验证了模型有效性。
- 证明这些模型可在资源受限设备上部署，适合实际应用。
随着硬件技术进步，物联网设备数量激增，广泛部署于大规模网络中，每日处理海量数据。然而，依赖边缘计算管理这些设备带来了显著安全漏洞，攻击者可通过单一设备渗透整个网络。面对日益严峻的网络安全威胁，尤其是僵尸网络攻击，本文研究了机器学习技术在边缘计算辅助物联网环境中的应用。具体而言，比较了随机森林、XGBoost和LightGBM三种先进的集成学习算法，以应对僵尸网络威胁的动态与复杂特性。利用包含正常与恶意实例的广泛认可物联网网络流量数据集，对模型进行训练、测试与评估，衡量其在检测和分类僵尸网络活动方面的准确性。此外，研究还探讨了这些模型在资源受限的边缘及物联网设备上的可行性，证明其在现实场景中的实用性。结果表明，机器学习可有效增强物联网网络抵御新兴网络安全挑战的能力。
原文摘要 · Abstract (English)
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.
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