A Hybrid Machine Learning Framework for Real-Time Network Intrusion Detection
DOI:
https://doi.org/10.66280/cset.v1i1.90Keywords:
Network Intrusion Detection, Hybrid Machine Learning, Cyber-Physical Systems, Systems Architecture, Algorithmic Governance, Infrastructure Sustainability, Socio-Technical Systems.Abstract
The rapid evolution of sophisticated cyber threats and the increasing heterogeneity of network traffic have rendered traditional signature-based intrusion detection systems largely insufficient for modern enterprise security. This paper proposes and analyzes a hybrid machine learning framework designed for real-time network intrusion detection, specifically engineered to balance the high-fidelity perception of deep learning with the computational efficiency of classical statistical models. We provide a comprehensive systems-level evaluation of the architectural trade-offs inherent in hybrid orchestration, focusing on the tension between detection depth and operational latency. The discussion extends into the socio-technical dimensions of cybersecurity infrastructure, addressing the requirements for robust data governance, the physicality of high-speed packet inspection, and the environmental sustainability of compute-intensive defense mechanisms. Furthermore, we examine the policy implications of automated threat mitigation, the ethical imperatives of fairness in algorithmic surveillance, and the necessity of transparent auditing for regulatory compliance in critical national infrastructures. By synthesizing perspectives from distributed systems, artificial intelligence, and public policy, this work offers a thorough conceptual roadmap for the next generation of resilient and adaptive security frameworks. We conclude that effective intrusion detection in the contemporary digital landscape is not merely a technical problem of pattern matching but a fundamental requirement of systemic governance, necessitating a holistic integration of technical precision, institutional accountability, and environmental stewardship.
References
1.Ahmad, I., et al. (2021). Deep learning for network intrusion detection: A systematic review. IEEE Access, 9, 102065-102081.
2.Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
3.Chen, Y., et al. (2019). Energy-efficient resource management in cloud computing: A survey. Journal of Systems and Software, 151, 1-22.
4.Cui, Z., et al. (2020). Detection of malicious network traffic based on deep learning. IEEE Transactions on Network and Service Management, 17(3), 1541-1552.
5.Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, (2), 222-232.
6.Devlin, J., et al. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
7.Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions. Journal of Econometrics, 182(1), 119-134.
8.Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
9.Garcia-Teodoro, P., et al. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), 18-28.
10.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
11.Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
12.Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems.
13.He, K., et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
14.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
15.Hull, J. C. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Pearson.
16.Jaworski, P., et al. (2020). Deep learning for network security: A survey. Journal of Network and Computer Applications, 151, 102479.
17.Katz, R. H. (2009). The information technology infrastructure for the 21st century. Communications of the ACM, 52(4), 11-13.
18.LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
19.Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.
20.Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for cybersecurity. IEEE Access, 7, 102177-102197.
21.Newman, M. E. J. (2010). Networks: An Introduction. Oxford University Press.
22.Paszke, A., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems.
23.Rossi, G. (2018). Socio-Technical Systems and the Finance Industry. Routledge.
24.Schwartz, R., et al. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
25.Shiller, R. J. (2015). Irrational Exuberance. Princeton University Press.
26.Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
27.Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
28.Xin, Y., et al. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365-35381.
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