Deep Learning-Based Network Intrusion Detection: A Comprehensive Framework for Cybersecurity Enhancement
Keywords:
deep learning, network intrusion detection, cybersecurity, system architecture, machine learning operations, adversarial robustness, governanceAbstract
The expanding complexity of enterprise networks, cloud infrastructures, and industrial control systems has revealed significant limitations in conventional signature-based network intrusion detection. Deep learning has become a promising alternative because it can learn hierarchical representations directly from high-dimensional and high-volume network traffic. However, a comprehensive deployment of deep learning for network intrusion detection requires more than an isolated algorithmic model. It demands system-level integration of data acquisition, feature processing, model selection, operational deployment, adversarial robustness, governance, and long-term sustainability. This paper presents a comprehensive framework for deep learning-based network intrusion detection that connects these technical and organizational dimensions. The framework examines architectural trade-offs among feed-forward, convolutional, recurrent, autoencoder, and attention-based models in relation to detection accuracy, temporal modeling, computational cost, interpretability, and adaptability. It also addresses data infrastructure, continuous retraining, adversarial stress testing, fairness, privacy, regulatory compliance, and energy sustainability. Rather than focusing on isolated performance benchmarks, the paper contributes a systemic perspective in which detection models operate within a larger socio-technical environment of security operations centers, network management systems, and policy controls. The discussion includes cross-domain insights from software-defined networking, critical infrastructure, and Internet of Things environments. The paper concludes that sustainable network intrusion detection depends on rigorous data curation, transparent evaluation, adversarial awareness, and organizational feedback loops. Future research directions are outlined for resilient, interpretable, and operationally viable deep learning intrusion detection systems.
References
1. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. 2010 IEEE Symposium on Security and Privacy, 305–316. https://doi.org/10.1109/SP.2010.25
2. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176. https://doi.org/10.1109/COMST.2015.2494502
3. Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine learning and deep learning methods for cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950
4. Ring, M., Wunderlich, S., Scheuring, D., Landes, D., & Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers & Security, 86, 147–167. https://doi.org/10.1016/j.cose.2019.06.005
5. Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50. https://doi.org/10.1109/TETCI.2017.2772792
6. Tang, T. A., Mhamdi, L., McLernon, D., Zaidi, S. A. R., & Ghogho, M. (2016). Deep learning approach for network intrusion detection in software defined networking. 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), 258–263. https://doi.org/10.1109/WINCOM.2016.7777224
7. Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418
8. Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system. Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies, 21–26. https://doi.org/10.4108/eai.3-12-2015.2262516
9. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1–6. https://doi.org/10.1109/CISDA.2009.5356528
10. Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy, 108–116. https://doi.org/10.5220/0006639801080116
11. Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems. Military Communications and Information Systems Conference (MilCIS), 1–6. https://doi.org/10.1109/MilCIS.2015.7348942
12. Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572. https://arxiv.org/abs/1412.6572
13. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016). The limitations of deep learning in adversarial settings. 2016 IEEE European Symposium on Security and Privacy (EuroS&P), 372–387. https://doi.org/10.1109/EuroSP.2016.36
14. Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., & Rieck, K. (2022). Dos and don'ts of machine learning in computer security. 31st USENIX Security Symposium (USENIX Security 22), 3971–3988. https://www.usenix.org/conference/usenixsecurity22/presentation/arp
15. Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493–501. https://doi.org/10.1007/s12083-017-0630-0
16. Doshi, R., Apthorpe, N., & Feamster, N. (2018). Machine learning DDoS detection for consumer Internet of Things devices. 2018 IEEE Security and Privacy Workshops (SPW), 29–35. https://doi.org/10.1109/SPW.2018.00013
17. Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1, e4150. https://doi.org/10.1002/ett.4150
18. Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. P. (2018). SoK: Security and privacy in machine learning. 2018 IEEE European Symposium on Security and Privacy (EuroS&P), 399–414. https://doi.org/10.1109/EuroSP.2018.00035
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