针对物联网入侵检测系统，提出三类优化提升准确率与部署效率。
Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures
- 用均衡采样和伪标签过滤改善数据分布不均问题。
- 准确率达95.45%，较基线提升6.26%，模型参数减少55%。
- 适合资源受限的物联网边缘设备部署，兼顾性能与轻量化。
物联网设备的快速普及催生了对自适应、低资源消耗入侵检测系统（IDS）的迫切需求，以应对动态演变的网络威胁。本文复现了发表于IEEE INFOCOM 2024的前沿自主在线IDS——AOC-IDS，其采用带有聚类排斥对比损失（CRC）的自编码器（AE）和基于高斯的自主决策模块，在UNSW-NB15基准上实现89.39%的准确率，与原文89.19%高度一致。我们识别出四方面局限：类别不平衡、伪标签不可靠、泛化能力弱及计算开销大，并针对性提出改进方案。XGBoost-BalSamp方法在UNSW-NB15上达到95.45%准确率，较基线提升6.26%；结合伪标签筛选（PseudoFilter）、Mixup增强（MixupAug）与轻量自编码器（LiteAE）的深度学习方案，最优结果达90.88%准确率（F1: 91.45%），优于原始模型，同时模型参数减少55%。实验表明，对AOC-IDS的针对性优化可显著提升准确率并增强在物联网边缘设备上的实用性。
原文摘要 · Abstract (English)
The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource-efficient Intrusion Detection Systems (IDS) capable of handling dynamic and evolving cyber threats. This paper investigates AOC-IDS, a state-of-the-art autonomous online IDS published at IEEE INFOCOM 2024, which employs an Autoencoder (AE) with Cluster Repelling Contrastive (CRC) loss and an autonomous Gaussian-based decision module. We first successfully replicate AOC-IDS on the UNSW-NB15 benchmark, achieving 89.39% accuracy in close agreement with the published 89.19%. We then identify four key limitations: class imbalance, unreliable pseudo-label generation, limited generalization, and computational overhead for IoT deployment, and propose targeted improvements for each. Our XGBoost-BalSamp method achieves 95.45% accuracy on UNSW-NB15, a gain of 6.26% over the baseline. Our combined deep learning approach (PseudoFilter, MixupAug, and LiteAE) achieves a best-run accuracy of 90.88% (F1: 91.45%), surpassing the base paper while reducing model parameters by 55%.These results demonstrate that targeted improvements to AOC-IDS yield consistent accuracy gains while improving practical deployability on IoT edge devices.
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