ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
AdvRoNIDS: A Constraint-Aware Framework for Adversarially Robust Network Intrusion Detection via Min-Max Training and Attribution Analysis
Akash Kushwaha1
Dr. Mohammed Abuzar A2
1 M.Sc, Data Science, SIES College of Arts, Science and Commerce (Empowered Autonomous), Mumbai, Maharashtra, India. 2 Head of Department, Data Science, SIES College of Arts, Science and Commerce (Empowered Autonomous), Mumbai, Maharashtra, India.
Published Online: September-December 2026
Pages: 290-298
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503034
References
1. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, "Intriguing Properties of Neural Networks," arXiv:1312.6199, 2013.
2. J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and Harnessing Adversarial Examples," arXiv:1412.6572, 2014.
3. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, "Towards Deep Learning Models Resistant to Adversarial Attacks," in Proc. ICLR, 2018, arXiv:1706.06083.
4. R. Sheatsley, B. Hoak, E. Pauley, Y. Beugin, M. J. Weisman, and P. McDaniel, "On the Robustness of Domain Constraints," in Proc. ACM SIGSAC CCS, 2021, pp. 495–515, arXiv:2105.08619.
5. R. Sheatsley, N. Papernot, M. J. Weisman, G. Verma, and P. McDaniel, "Adversarial Examples for Network Intrusion Detection Systems," Journal of Computer Security, vol. 30, no. 5, pp. 727–752, 2022.
6. M. Teuffenbach, E. Piatkowska, and P. Smith, "Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints," in Proc. CD-MAKE, Springer LNCS, 2020, pp. 301–320.
7. S. Ennaji, F. De Gaspari, D. Hitaj, A. Kbidi, and L. V. Mancini, "Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects," arXiv:2409.18736, 2024; IEEE Access, vol. 13, p. 148613, 2025.
8. Debicha, B. Cochez, T. Kenaza, T. Debatty, J.-M. Dricot, and W. Mees, "Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems," arXiv:2303.07003, 2023.
9. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization," in Proc. ICISSP, Portugal, 2018.
10. Canadian Institute for Cybersecurity and Communications Security Establishment, "A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018)," University of New Brunswick, 2018.
11. S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Proc. NeurIPS, 2017, pp. 4765–4774.
12. Carlini and D. Wagner, "Towards Evaluating the Robustness of Neural Networks," in Proc. IEEE Symposium on Security and Privacy (SP), 2017, pp. 39–57, doi:10.1109/SP.2017.49.
13. S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, "DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks," in Proc. IEEE CVPR, 2016, pp. 2574–2582, doi:10.1109/CVPR.2016.282.
14. Kurakin, I. Goodfellow, and S. Bengio, "Adversarial Examples in the Physical World," arXiv:1607.02533, 2016.
15. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, "The Limitations of Deep Learning in Adversarial Settings," in Proc. IEEE EuroS&P, 2016, pp. 372–387, doi:10.1109/EuroSP.2016.36.
16. Biggio and F. Roli, "Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning," Pattern Recognition, vol. 84, pp. 317–331, Dec. 2018, doi:10.1016/j.patcog.2018.07.023.
17. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," in Proc. IEEE CISDA, Ottawa, Canada, 2009,
18. pp. 1–6, doi:10.1109/CISDA.2009.5356528.
19. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002, doi:10.1613/jair.953.
20. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, and M. Marchetti, "On the Effectiveness of Machine and Deep Learning for Cyber Security," in Proc. 10th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia, 2018, pp. 371–390,
21. doi:10.23919/CYCON.2018.8405026.
22. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525–41550, 2019, doi:10.1109/ACCESS.2019.2895334.
2. J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and Harnessing Adversarial Examples," arXiv:1412.6572, 2014.
3. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, "Towards Deep Learning Models Resistant to Adversarial Attacks," in Proc. ICLR, 2018, arXiv:1706.06083.
4. R. Sheatsley, B. Hoak, E. Pauley, Y. Beugin, M. J. Weisman, and P. McDaniel, "On the Robustness of Domain Constraints," in Proc. ACM SIGSAC CCS, 2021, pp. 495–515, arXiv:2105.08619.
5. R. Sheatsley, N. Papernot, M. J. Weisman, G. Verma, and P. McDaniel, "Adversarial Examples for Network Intrusion Detection Systems," Journal of Computer Security, vol. 30, no. 5, pp. 727–752, 2022.
6. M. Teuffenbach, E. Piatkowska, and P. Smith, "Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints," in Proc. CD-MAKE, Springer LNCS, 2020, pp. 301–320.
7. S. Ennaji, F. De Gaspari, D. Hitaj, A. Kbidi, and L. V. Mancini, "Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects," arXiv:2409.18736, 2024; IEEE Access, vol. 13, p. 148613, 2025.
8. Debicha, B. Cochez, T. Kenaza, T. Debatty, J.-M. Dricot, and W. Mees, "Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems," arXiv:2303.07003, 2023.
9. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization," in Proc. ICISSP, Portugal, 2018.
10. Canadian Institute for Cybersecurity and Communications Security Establishment, "A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018)," University of New Brunswick, 2018.
11. S. M. Lundberg and S.-I. Lee, "A Unified Approach to Interpreting Model Predictions," in Proc. NeurIPS, 2017, pp. 4765–4774.
12. Carlini and D. Wagner, "Towards Evaluating the Robustness of Neural Networks," in Proc. IEEE Symposium on Security and Privacy (SP), 2017, pp. 39–57, doi:10.1109/SP.2017.49.
13. S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, "DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks," in Proc. IEEE CVPR, 2016, pp. 2574–2582, doi:10.1109/CVPR.2016.282.
14. Kurakin, I. Goodfellow, and S. Bengio, "Adversarial Examples in the Physical World," arXiv:1607.02533, 2016.
15. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, "The Limitations of Deep Learning in Adversarial Settings," in Proc. IEEE EuroS&P, 2016, pp. 372–387, doi:10.1109/EuroSP.2016.36.
16. Biggio and F. Roli, "Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning," Pattern Recognition, vol. 84, pp. 317–331, Dec. 2018, doi:10.1016/j.patcog.2018.07.023.
17. M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," in Proc. IEEE CISDA, Ottawa, Canada, 2009,
18. pp. 1–6, doi:10.1109/CISDA.2009.5356528.
19. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002, doi:10.1613/jair.953.
20. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, and M. Marchetti, "On the Effectiveness of Machine and Deep Learning for Cyber Security," in Proc. 10th International Conference on Cyber Conflict (CyCon), Tallinn, Estonia, 2018, pp. 371–390,
21. doi:10.23919/CYCON.2018.8405026.
22. R. Vinayakumar, M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, and S. Venkatraman, "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525–41550, 2019, doi:10.1109/ACCESS.2019.2895334.
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