标题： 利用机器学习在边缘计算辅助的物联网网络中检测僵尸网络攻击 显示英文标题

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

摘要： 物联网设备的增加，由硬件技术的进步所推动，导致在处理每日大量数据的大规模网络中广泛部署。 然而，依赖边缘计算来管理这些设备引入了重大的安全漏洞，因为攻击者可以通过攻击单个物联网设备来破坏整个网络。 鉴于日益严峻的网络安全威胁，特别是僵尸网络攻击，本文研究了机器学习技术在增强边缘计算辅助物联网环境中的安全性的应用。 具体而言，它对随机森林、XGBoost和LightGBM三种先进的集成学习算法进行了比较分析，以应对僵尸网络威胁的动态性和复杂性。 利用一个广受认可的物联网网络流量数据集，包含良性和恶意实例，这些模型被训练、测试和评估其在检测和分类僵尸网络活动方面的准确性。 此外，该研究探讨了在资源受限的边缘和物联网设备上部署这些模型的可行性，展示了它们在现实场景中的实际适用性。 结果突显了机器学习在加强物联网网络应对新兴网络安全挑战方面的潜力。 显示英文摘要

摘要： 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.