Uncertainty-Aware Evidential Adversarial Defense for Cloud Intrusion Detection under GAN-Based Evasion Attacks
DOI:
https://doi.org/10.54097/6bm9sm08Keywords:
Intrusion detection, cloud security, adversarial robustness, adversarial training, uncertainty quantification, evidential deep learning, selective prediction, GAN evasionAbstract
Cloud intrusion detection systems (IDSs) increasingly depend on deep neural classifiers, which are vulnerable to adversarial evasion attacks—including realistic, GAN-crafted traffic—that flip malicious flows to benign with near-imperceptible, functionally consistent perturbations. Adversarial training (AT) hardens the decision boundary but yields overconfident point predictions: a hardened detector still cannot signal when an input is suspicious, and Bayesian or ensemble uncertainty estimators that could do so require many forward passes, which is impractical at cloud line rate. We propose UA-EAD, an uncertainty-aware evidential adversarial defense that (i) equips the detector with an evidential (Dirichlet) head yielding calibrated predictive uncertainty in a single forward pass, (ii) trains it with an uncertainty-weighted adversarial objective plus a consistency regularizer that concentrates robustness on the most uncertain, near-boundary flows, and (iii) uses the resulting uncertainty for selective prediction, abstaining on inputs it cannot confidently classify. On NSL-KDD under FGSM, BIM, PGD, C&W, and a black-box WGAN-GP transfer attack, UA-EAD matches the strongest AT baselines in robust accuracy (97.4%) while attaining the highest clean accuracy (98.1%). Its single-pass evidential uncertainty equals maximum-softmax-probability and surpasses Monte-Carlo Dropout for adversarial detection at 21x lower inference cost, and yields the best adversarial-detection AUROC among robust models. Selective prediction lifts accuracy on 17 novel (out-of-distribution) attack types from 76.9% to 87.0% at 70% coverage. We further report an empirical robustness–detectability trade-off that clarifies why hardening alone is insufficient for trustworthy cloud IDS.
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[1] Zi, B. (2024). Cloud-native distributed systems for real-time payment intelligence. AI and Data Science Journal, 1(1), 51–56.
[2] Chen, Z., Wang, M., Zeng, Z., & Ping, W. (2026). Uncertainty-aware financial forecasting: Leveraging conformal prediction for risk-adjusted models. IEEE Access, 14, 74210–74221. https://doi.org/10.1109/ACCESS.2026.3591245
[3] Wang, B., Wang, Z., Zhao, W., Zhang, F., & Shang, W. (2026). DRL-Adapt: Deep reinforcement learning for adaptive routing convergence optimization in large-scale networks. IEEE Open Journal of the Computer Society, 7, 261–270. https://doi.org/10.1109/OJCS.2026.3612745
[4] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. In Proceedings of the International Conference on Learning Representations.
[5] Teng, D. (2025). PACO: Predictive auto-configuration for SLO-constrained large language model inference serving. Innovation and Technology Studies.
[6] Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks. In Proceedings of the International Conference on Learning Representations.
[7] Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. In 2017 IEEE Symposium on Security and Privacy (pp. 39–57). https://doi.org/10.1109/SP.2017.21
[8] Kurakin, A., Goodfellow, I. J., & Bengio, S. (2017). Adversarial examples in the physical world. In ICLR 2017 Workshop Track.
[9] Moosavi-Dezfooli, S.-M., Fawzi, A., & Frossard, P. (2016). DeepFool: A simple and accurate method to fool deep neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2574–2582). https://doi.org/10.1109/CVPR.2016.282
[10] Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (Vol. 27, pp. 2672–2680).
[11] Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning (pp. 214–223).
[12] Gulrajani, F., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. C. (2017). Improved training of Wasserstein GANs. In Advances in Neural Information Processing Systems (Vol. 30, pp. 5767–5777).
[13] Lin, Z., Shi, Y., & Xue, Z. (2022). IDSGAN: Generative adversarial networks for attack generation against intrusion detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 79–91).
[14] Apruzzese, G., Andreolini, M., Ferretti, L., Marchetti, M., & Colajanni, M. (2022). Modeling realistic adversarial attacks against network intrusion detection systems. Digital Threats: Research and Practice, 3(3), 1–19. https://doi.org/10.1145/3484701
[15] Ding, J., Shen, Z., & Liu, W. (2026). Game-theoretic cost-sensitive adversarial training for robust cloud intrusion detection against GAN-based evasion attacks. Applied Sciences, 16(8), 3944. https://doi.org/10.3390/app16083944
[16] Zhang, H., Yu, Y., Jiao, J., Xing, E. P., El Ghaoui, L., & Jordan, M. I. (2019). Theoretically principled trade-off between robustness and accuracy. In Proceedings of the 36th International Conference on Machine Learning (pp. 7472–7482).
[17] Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2018). Ensemble adversarial training: Attacks and defenses. In Proceedings of the International Conference on Learning Representations.
[18] Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (pp. 2980–2988). https://doi.org/10.1109/ICCV.2017.324
[19] Sensoy, M., Kaplan, L., & Kandemir, M. (2018). Evidential deep learning to quantify classification uncertainty. In Advances in Neural Information Processing Systems (Vol. 31, pp. 3179–3189).
[20] Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the 33rd International Conference on Machine Learning (pp. 1050–1059).
[21] Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in Neural Information Processing Systems (Vol. 30, pp. 6402–6413).
[22] Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight uncertainty in neural networks. In Proceedings of the 32nd International Conference on Machine Learning (pp. 1613–1622).
[23] Kendall, A., & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems (Vol. 30, pp. 5574–5584).
[24] Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (pp. 1321–1330).
[25] Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. In Proceedings of the International Conference on Learning Representations.
[26] Feinman, R., Curtin, R. R., Shintre, S., & Gardner, A. B. (2017). Detecting adversarial samples from artifacts. arXiv:1703.00410.
[27] Smith, L., & Gal, Y. (2018). Understanding measures of uncertainty for adversarial example detection. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (pp. 560–569).
[28] Amini, A., Schwarting, W., Soleimany, A., & Rus, D. (2020). Deep evidential regression. In Advances in Neural Information Processing Systems (Vol. 33, pp. 14927–14937).
[29] Wang, Z., Yang, J. S., Shang, W., & Ding, J. (2026). FairPromote: Explainable and fairness-aware talent promotion prediction via adversarial debiasing and SHAP-based interpretation. IEEE Access, 14, 72890–72904. https://doi.org/10.1109/ACCESS.2026.3583411
[30] Zhang, F., Guo, Z., Ding, J., Yang, J., & Liu, W. (2026). Adaptive sensor fusion for robust perception in dense fog: A gated vision and LiDAR integration framework. Sensors, 26(12), 3728. https://doi.org/10.3390/s26123728
[31] Zi, B. (2024). Large language models for enterprise workflow automation in financial operations. Innovation and Technology Studies, 1(1), 24–29.
[32] Teng, D., Rhee, M., Qin, Y., Zi, B., & Liu, W. (2026). SW-SpeedDLM: Sliding window speculative decoding for diffusion language models under long context constraints. Mathematics, 14(12), 2137. https://doi.org/10.3390/math14122137
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