An intelligent intrusion detection system for cyber-physical systems using GAN-LSTM networks

Résumé fourni par la source

Cyber-Physical Systems (CPS) face increasing cybersecurity threats, demanding advanced intrusion detection methods. This research proposes a novel GAN-LSTM hybrid model to enhance anomaly detection in CPS by addressing key limitations of traditional approaches, including class imbalance and temporal dependency learning. The primary objectives are: (i) developing an adversarial learning framework where the generator synthesizes realistic attack patterns while the discriminator improves detection robustness, (ii) introducing a hybrid anomaly scoring mechanism combining reconstruction and discrimination loss, and (iii) validating performance on real-world CPS datasets (SWaT and WADI). The model achieves 87% accuracy (SWaT) and 91% accuracy (WADI), with precision reaching 93% (SWaT) and 97% (WADI)—demonstrating strong capability to minimize false alarms. Notably, it attains 99% recall on SWaT, ensuring near-complete attack detection, though WADI recall remains lower (75%) due to complex attack patterns. The balanced F1-scores (91% SWaT, 82% WADI) outperform state-of-the-art methods like MAD-GAN by 14–45%. Key innovations include LSTM-based temporal feature learning and GAN-driven synthetic minority oversampling, effectively handling CPS-specific challenges such as multivariate time-series complexity and rare attack instances. These results highlight the model’s potential for real-world CPS security, while future work will address deployment constraints like computational latency.

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