用半监督学习提升数据不平衡下的DDoS攻击检测能力
A Study on Semi-Supervised Detection of DDoS Attacks under Class Imbalance
- 采用13种先进半监督算法应对标签稀缺与类别失衡问题
- 在极端环境下验证算法有效性，发现部分方法表现显著下降
- 为构建鲁棒的智能入侵检测系统提供实证参考
网络安全中最具挑战性的问题之一是消除分布式拒绝服务（DDoS）攻击。利用人工智能自动化这一任务面临固有的类别不平衡和真实世界数据集标注样本不足的难题。本研究探讨了半监督学习（SSL）技术在数据不平衡且部分标记的情况下提升DDoS攻击检测的效果。评估了13种前沿的SSL算法在多种场景下的表现，分析其实际效能与局限性，包括在极端环境下的适应能力。结果有助于设计对类别不平衡具有鲁棒性并能处理部分标注数据的智能入侵检测系统（IDS）。
原文摘要 · Abstract (English)
One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex process due to the inherent class imbalance and lack of sufficient labeled samples of real-world datasets. This research investigates the use of Semi-Supervised Learning (SSL) techniques to improve DDoS attack detection when data is imbalanced and partially labeled. In this process, 13 state-of-the-art SSL algorithms are evaluated for detecting DDoS attacks in several scenarios. We evaluate their practical efficacy and shortcomings, including the extent to which they work in extreme environments. The results will offer insight into designing intelligent Intrusion Detection Systems (IDSs) that are robust against class imbalance and handle partially labeled data.
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