AnchorMixGAN：云集成物联网网络中DDoS检测的锚定对齐生成式半监督方法
AnchorMixGAN: Anchor-Aligned Generative Semi-Supervision for DDoS Detection in Cloud-Integrated IoT Networks
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中文总结 AI 辅助
针对云集成物联网中标记数据稀缺的DDoS检测，提出AnchorMixGAN生成式半监督框架，通过锚定对齐目标构建和MixUp混合，在三个基准上超越MixGAN。
中文摘要 AI 辅助
在云集成物联网网络中，当标记流量稀缺时，检测分布式拒绝服务（DDoS）攻击十分困难。生成式半监督学习可以补充可用的训练数据，但合成视图引起的预测偏移可能会影响分配给真实未标记流的目标。我们提出了AnchorMixGAN，一种生成式半监督框架，通过锚定对齐的目标构建来解决此问题。其Anchor-MAS模块将每个真实未标记流视为锚点，并通过每次用生成流量中的值替换一个字段组来创建替代视图。一个冻结的参考分类器预测锚点及其视图；对这些预测进行平均和锐化，为原始流产生软目标。然后，使用MixUp将该流及其目标与一个标记示例混合，使检测器能够同时从原始标记记录和混合示例中学习。我们分析了参考分类器误差、视图构建和锐化对目标的影响，并在固定检测器预测下推导了交叉熵变化的界限。在报告的90%训练设置下，标记20%的训练记录，AnchorMixGAN在NSLKDD、BoT-IoT和CICIoT2023上分别达到97.3%、97.4%和96.5%的准确率，分别超过相应的MixGAN结果1.6、1.0和4.4个百分点。
英文摘要
Detecting distributed denial-of-service (DDoS) attacks in cloud-integrated IoT networks is difficult when labeled traffic is scarce. Generative semi-supervised learning can supplement the available training data, but prediction shifts induced by synthetic views may affect the targets assigned to real unlabeled flows. We propose AnchorMixGAN, a generative semi-supervised framework that addresses this problem through anchor-aligned target construction. Its Anchor-MAS module treats each real unlabeled flow as an anchor and creates alternative views by replacing one field group at a time with values from generated traffic. A frozen reference classifier predicts the anchor and its views; averaging and sharpening these predictions produces a soft target for the original flow. The flow and its target are then mixed with a labeled example using MixUp, allowing the detector to learn from both the original labeled records and the mixed examples. We analyze how reference-classifier error, view construction, and sharpening affect the target, and derive a bound on the resulting change in cross-entropy at a fixed detector prediction. At the reported 90% training setting with 20% of the training records labeled, AnchorMixGAN attains accuracies of 97.3%, 97.4%, and 96.5% on NSLKDD, BoT-IoT, and CICIoT2023, respectively, exceeding the corresponding MixGAN results by 1.6, 1.0, and 4.4 percentage points.
