arXiv 2504.043672025-04-19cs.CRcs.AI

WeiDetect：基于威布尔分布的联邦学习网络入侵检测系统投毒攻击防御

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems

Department of Computer Applications, Cochin University of Science and Technology（科钦大学科学与技术学院计算机应用系）；Department of Mathematics, University of Padua（帕多瓦大学数学系）；University of Campinas (Unicamp)（坎皮纳斯大学）

Sameera K. M., Vinod P., Anderson Rocha, Rafidha Rehiman K. A., Mauro Conti

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AI总结：

针对联邦学习网络入侵检测中的投毒攻击，提出服务器端两阶段防御机制WeiDetect，利用威布尔分布分析验证分数以识别恶意客户端，在非独立同分布数据下显著提升召回率与F1分数。

摘要：

在数据扩张的时代，确保数据隐私变得日益关键，对传统的基于人工智能的应用构成了重大挑战。此外，物联网设备的日益普及带来了显著的网络安全挑战，使得传统的网络入侵检测系统（NIDS）在应对不断演变的威胁时效果减弱，而隐私问题和监管限制也制约了其部署。联邦学习（FL）作为一种有前景的解决方案应运而生，它允许在保持数据隐私的同时进行分散式模型训练，以解决这些问题。然而，尽管实施了隐私保护技术，FL系统仍然容易受到对抗性攻击。此外，在FL场景中，客户端之间的数据分布并非异构。我们提出了WeiDetect，一种用于基于FL的NIDS的两阶段、服务器端防御机制，用于检测恶意参与者以应对这些挑战。在第一阶段，使用验证数据集评估本地模型以生成验证分数。然后使用威布尔分布对这些分数进行分析，识别并移除恶意模型。我们进行了实验，以评估我们的方法在不同攻击场景下的有效性。我们的评估包括两个流行的数据集，CIC-Darknet2020和CSE-CIC-IDS2018，在非独立同分布（non-IID）数据分布下进行测试。我们的研究结果表明，WeiDetect优于最先进的防御方法，将目标类别的召回率提高高达70%，并将全局模型的F1分数提高1%至14%。

英文摘要：

In the era of data expansion, ensuring data privacy has become increasingly critical, posing significant challenges to traditional AI-based applications. In addition, the increasing adoption of IoT devices has introduced significant cybersecurity challenges, making traditional Network Intrusion Detection Systems (NIDS) less effective against evolving threats, and privacy concerns and regulatory restrictions limit their deployment. Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training while maintaining data privacy to solve these issues. However, despite implementing privacy-preserving technologies, FL systems remain vulnerable to adversarial attacks. Furthermore, data distribution among clients is not heterogeneous in the FL scenario. We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants to address these challenges. In the first phase, local models are evaluated using a validation dataset to generate validation scores. These scores are then analyzed using a Weibull distribution, identifying and removing malicious models. We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings. Our evaluation included two popular datasets, CIC-Darknet2020 and CSE-CIC-IDS2018, tested under non-IID data distributions. Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches, improving higher target class recall up to 70% and enhancing the global model's F1 score by 1% to 14%.

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