用生成模型提升物联网罕见攻击检测率
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
- 用条件表格式GAN生成稀有攻击数据，缓解样本不平衡
- 在CSE-CIC-IDS2018上实现99.90%整体准确率，罕见攻击检测率达80%
- 适合研究异常检测与安全防御的工程师和研究人员
6G技术推动物联网（IoT）网络在各行业广泛应用，但其普及也带来重大安全风险，尤其体现在稀有但破坏性大的网络攻击检测难题。传统入侵检测系统（IDS）因物联网数据存在严重类别不平衡，难以有效识别稀有攻击。本文提出一种两阶段新型系统——条件表格式生成对抗网络增强的多类分类器（CTGSM-DNN）。第一阶段采用条件表格式生成对抗网络（CTGAN）为稀有攻击类别生成合成数据；第二阶段使用SMOTEENN方法优化数据质量。实验基于CSE-CIC-IDS2018数据集进行，结果表明该系统在多类分类任务中整体准确率达99.90%，对稀有攻击的检测准确率达到80%。
原文摘要 · Abstract (English)
Internet of things (IoT) networks, boosted by 6G technology, are transforming various industries. However, their widespread adoption introduces significant security risks, particularly in detecting rare but potentially damaging cyber-attacks. This makes the development of robust IDS crucial for monitoring network traffic and ensuring their safety. Traditional IDS often struggle with detecting rare attacks due to severe class imbalances in IoT data. In this paper, we propose a novel two-stage system called conditional tabular generative synthetic minority data generation with deep neural network (CTGSM-DNN). In the first stage, a conditional tabular generative adversarial network (CTGAN) is employed to generate synthetic data for rare attack classes. In the second stage, the SMOTEENN method is applied to improve dataset quality. The full study was conducted using the CSE-CIC-IDS2018 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results demonstrated the effectiveness of the proposed multiclass classifier, achieving an overall accuracy of 99.90% and 80% accuracy in detecting rare attacks.
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