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AI-Driven Defense Mechanism for Lattice-Based Post-Quantum Cryptography: Adaptive Mitigation Against Side-Channel Attacks

11 pagesPublished: August 21, 2025

Abstract

Post-quantum cryptography (PQC) offers resistance against quantum adversaries. This study's practical implementations remain vulnerable to side-channel attacks (SCAs) that exploit timing, power, or electromagnetic leakage. In this study, we introduce an unsupervised, resource-efficient anomaly detection framework tailored to the unique constraints of post-quantum cryptography (PQC) systems. Unlike traditional methods that rely on labeled attack traces or algorithm-specific profiling, our approach leverages an autoencoder trained solely on benign traces to learn deep latent representations of normal cryptographic behavior. The system flags deviations using reconstruction error and supports multiple PQC schemes, including Kyber and Dilithium, without retraining. Experimental results demonstrate an average classification accuracy of 98.1%, with a false positive rate of 0.7% and a false negative rate of 0.4%. Under adversarial perturbation and Gaussian noise, the model maintains an AUC-ROC of 1.00, confirming its robustness. Additionally, ablation studies across CNN, GRU, and Transformer architectures validate the autoencoder’s superior trade-off between accuracy and latency, achieving an inference time of 0.036 ms and a model size of only 0.11 MB. This enables real-time deployment on constrained devices without sacrificing security. The proposed solution marks a step forward in scalable, adaptive post-quantum defenses and opens new directions for cryptographic anomaly detection with minimal overhead. This framework is deployable on real-world PQC-enabled IoT systems.

Keyphrases: autoencoder based security, lightweight real time defense, post quantum cryptography, side channel attack detection, unsupervised anomaly detection

In: Akira Yamada, Huy Kang Kim, Yujue Wang and Tung-Tso Tsai (editors). Proceedings of the 20th Asia Joint Conference on Information Security, vol 106, pages 176-186.

BibTeX entry
@inproceedings{AsiaJCIS2025:AI_Driven_Defense_Mechanism,
  author    = {Wibby Aldryani Astuti Praditasari and Hyungyeop Kim and Hyejin Yoon and Danang Rimbawa and Okyeon Yi},
  title     = {AI-Driven Defense Mechanism for Lattice-Based Post-Quantum Cryptography: Adaptive Mitigation Against Side-Channel Attacks},
  booktitle = {Proceedings of the 20th Asia Joint Conference on Information Security},
  editor    = {Akira Yamada and Huy Kang Kim and Yujue Wang and Tung-Tso Tsai},
  series    = {EPiC Series in Computing},
  volume    = {106},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/rC5W},
  doi       = {10.29007/qxrb},
  pages     = {176-186},
  year      = {2025}}
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