Bias and Data Privacy: Challenges in AI-Driven Network Security: A Statistical Assessment using Synthetic Real-World Data

The introduction of artificial intelligence (AI) into network security has enabled significant innovations in intrusion detection, threat classification, and the application of access controls. Although these advantages exist, AI models are susceptible to systemic bias and can pose a significant threat to data privacy when implemented at scale. In this paper, statistical analysis of bias, privacy leakage, and discriminatory consequences in AI-based network threat detection systems is provided based on a synthetic data-set that is simulated on a real-world corpus of intrusion detection. Findings have shown that (1) biased training data cause unrepresentative false-positive and false-negative rates across user groups, (2) the models that are not trained with privacy-preserving mechanisms have quantifiable privacy leakage through membership inference attacks, and (3) the results of algorithmic decisions are unequal between geographic and demographic groups based on data imbalance. These results highlight the need for a representative data-set, differentiated privacy, strong security measures, and clear ethical standards to prevent harm. The research provides a systematic framework for how auditors should conduct bias and privacy vulnerability audits in the context of network security enabled by AI.

Bias ,Data, Privacy, Challenges, AI-Driven

1. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org. [Google Scholar] [Crossref]

2. Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019). A survey of deep learning methods for cyber security. Information, 10(4), 122. [Google Scholar] [Crossref]

3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [Google Scholar] [Crossref]

4. Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine-learning models. IEEE Symposium on Security and Privacy, 3–18. [Google Scholar] [Crossref]

5. Tjoa, E., & Guan, C. (2020). A survey on explainable artificial intelligence (XAI). IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. [Google Scholar] [Crossref]

6. Zhang, J., & Meng, W. (2021). Artificial intelligence in network intrusion detection: A systematic review. Computer Communications, 168, 94–109. [Google Scholar] [Crossref]

7. Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer. [Google Scholar] [Crossref]

8. Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018). The secret sharer: Evaluating and testing unintended memorization in neural networks. USENIX Security Symposium, 267–284. [Google Scholar] [Crossref]

9. Carlini, N., Mishra, P., Vaidya, T., Zhang, Y., Sherr, M., Shields, C., ... & Wagner, D. (2016). Hidden voice commands. 25th USENIX Security Symposium, 513–530. [Google Scholar] [Crossref]

10. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163. [Google Scholar] [Crossref]

11. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. [Google Scholar] [Crossref]

12. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407. [Google Scholar] [Crossref]

13. Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and removing disparate impact. ACM Conference on Fairness, Accountability, and Transparency, 259– 268. [Google Scholar] [Crossref]

14. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [Google Scholar] [Crossref]

15. Kairouz, P., McMahan, H. B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. [Google Scholar] [Crossref]

16. Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–705. [Google Scholar] [Crossref]

17. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. [Google Scholar] [Crossref]

18. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. [Google Scholar] [Crossref]

19. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … & Gebru, T. (2019). Model cards for model reporting. ACM Conference on Fairness, Accountability, and Transparency, 220– 229. [Google Scholar] [Crossref]

20. Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of Machine Learning. MIT Press. [Google Scholar] [Crossref]

21. Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I., & Talwar, K. (2017). Semi-supervised knowledge transfer for deep learning from private training data. ICLR, 1–17. [Google Scholar] [Crossref]

22. Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. AAAI/ACM Conference on AI Ethics and Society, 429–435. [Google Scholar] [Crossref]

23. Rigaki, M., & Garcia, S. (2018). Bringing a GAN to a knife-fight: Adapting malware communication to avoid detection. IEEE Security and Privacy Workshops, 70–75. [Google Scholar] [Crossref]

24. Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine-learning models. IEEE Symposium on Security and Privacy, 3–18. [Google Scholar] [Crossref]

25. Stolfo, S., Fan, W., Lee, W., Prodromidis, A., & Chan, P. (2000). Cost-based modeling for fraud and intrusion detection. IEEE Computer Security Applications Conference, 14–23. [Google Scholar] [Crossref]

26. Tjoa, E., & Guan, C. (2020). A survey on explainable artificial intelligence (XAI). IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. [Google Scholar] [Crossref]

27. Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., & Venkatraman, S. (2019). Deep learning for malicious URL detection. IEEE Access, 7, 163268–163284. [Google Scholar] [Crossref]

28. Meng, W. (2021). Artificial intelligence in network intrusion detection: A systematic review. Computer Communications, 168, 94–109. [Google Scholar] [Crossref]

29. Zhou, Z.-H. (2021). Machine Learning. Springer. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet