Security Threat Detection and Defense Theory of Intelligent Operation and Maintenance System for Rail Transit Based on Adversarial Machine Learning

Summary

This study proposes PGD-CLA-ADS, an adversarial machine learning framework for detecting and defending against threats to rail transit intelligent operation and maintenance systems. The model generates simulated attacks with enhanced PGD samples and uses a CNN-LSTM-attention network to extract attack features, then applies an adaptive defense for real-time response. Tested on public datasets, it reports 95.8% threat detection accuracy, a 2.1% false positive rate, and attack response latency within 0.3 s.

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