Deep Models against Deep Threats: A Review of Deep Learning for Cyber Attack Mitigation

The speed of the advancement of digital technologies has led to a remarkable rise in the scale, complexity, and sophistication of cyber-attacks. Traditional security approaches based on extensive use of static rules and signature-based detections are simply not adequate for contemporary threats such as zero-day exploits, advanced persistent threats (APTs), and adversarial attacks. As a radical shift in approach, deep learning has emerged, providing intelligent, adaptive, and automated features for cyber defenses. This review paper provides an inclusive overview of deep learning technologies to mitigate various forms of cyber-attacks. Various deep learning architectures are investigated in this paper, such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), autoencoders, generative adversarial networks (GANs), and graph neural networks (GNN), and the applications of each technology in intrusion detection, malware classification, phishing detection, anomaly detection, and network traffic analysis. In addition, we discuss mainstream datasets, metrics for evaluation, and methodology as they relate to cyber security. Issues such as limited data, adversarial robustness, and usability, will be critically discussed. We also discuss new directions such as hybrid models, privacy-preserving learning, and federated defenses. By aggregating recent research, this review highlights the promise of deep models to serve as robust defenders against deep and evolving cyber-attacks, which serves as impetus for next-generation intelligent cybersecurity systems.

Deep Learning, Cybersecurity, Cyber Attack Mitigation, Intrusion Detection, Malware Detection

1. Li, P., Xu, C., Xu, H., Dong, L., & Wang, R. (2019). Research on data privacy protection algorithm with homomorphism mechanism based on redundant slice technology in wireless sensor networks. China Communications, 16(5), 158–170. [Google Scholar] [Crossref]

2. Al Rasyid, M. U. H., Prasetyo, D., Nadhori, I. U., & Alasiry, A. H. (2015). Mobile monitoring of muscular strain sensor based on Wireless Body Area Network. In 2015 International Electronics Symposium (IES) (pp. 284–287). IEEE. [Google Scholar] [Crossref]

3. Nelson, J., et al. (2021). Wireless Sensor Network with Mesh Topology for Carbon Dioxide Monitoring in a Winery. In 2021 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT) (pp. 30–33). IEEE. [Google Scholar] [Crossref]

4. Wang, H., Yang, G., Xu, J., Chen, Z., Chen, L., & Yang, Z. (2011). A novel data collection approach for Wireless Sensor Networks. In 2011 International Conference on Electrical and Control Engineering (pp. 4287–4290). IEEE. [Google Scholar] [Crossref]

5. Arumugasamy, S. (2024). An intrusion detection approach in wireless sensor network security through CNN-BiLSTM model. Journal of Theoretical and Applied Information Technology, 102(2). [Google Scholar] [Crossref]

6. Hu, R. (2016). Key Technology for Big Visual Data Analysis in Security Space and Its Applications. In 2016 International Conference on Advanced Cloud and Big Data (CBD) (p. 333). IEEE. [Google Scholar] [Crossref]

7. Wang, X., Herwono, I., Cerbo, F. D., Kearney, P., & Shackleton, M. (2018). Enabling cyber security data sharing for large-scale enterprises using managed security services. In 2018 IEEE Conference on Communications and Network Security (CNS) (pp. 1–7). IEEE. [Google Scholar] [Crossref]

8. Diaba, S. Y., Shafie-Khah, M., & Elmusrati, M. (2023). Cyber security in power systems using meta-heuristic and deep learning algorithms. IEEe Access, 11, 18660-18672. [Google Scholar] [Crossref]

9. Bhuvaneshwari, A. J., & Kaythry, P. (2023). A review of deep learning strategies for enhancing cybersecurity in networks: deep learning strategies for enhancing cybersecurity. Journal of Scientific & Industrial Research (JSIR), 82(12), 1316-1330. [Google Scholar] [Crossref]

10. Torre, D., Mesadieu, F., & Chennamaneni, A. (2023). Deep learning techniques to detect cybersecurity attacks: a systematic mapping study. Empirical Software Engineering, 28(3), 76. [Google Scholar] [Crossref]

11. Hussen, N., Elghamrawy, S. M., Salem, M., & El-Desouky, A. I. (2023). A fully streaming big data framework for cyber security based on optimized deep learning algorithm. IEEE Access, 11, 65675-65688. [Google Scholar] [Crossref]

12. Udayakumar, R., Joshi, A., Boomiga, S. S., & Sugumar, R. (2023). Deep fraud Net: A deep learning approach for cyber security and financial fraud detection and classification. Journal of Internet Services and Information Security, 13(3), 138-157. [Google Scholar] [Crossref]

13. Kanagala, P. (2023). Effective cyber security system to secure optical data based on deep learning approach for healthcare application. Optik, 272, 170315. [Google Scholar] [Crossref]

14. Dey, S., Sarma, W., & Tiwari, S. (2023). Deep learning applications for real-time cybersecurity threat analysis in distributed cloud systems. World Journal of Advanced Research and Reviews, 17(3), 1044-1058. [Google Scholar] [Crossref]

15. Szynkiewicz, W., Niewiadomska-Szynkiewicz, E., & Lis, K. (2023). Deep learning of sensor data in cybersecurity of robotic systems: overview and case study results. Electronics, 12(19), 4146. [Google Scholar] [Crossref]

16. Sarker, I. H. (2021). Deep cybersecurity: a comprehensive overview from neural network and deep learning perspective. SN Computer Science, 2(3), 154. [Google Scholar] [Crossref]

17. Zhang, J., Pan, L., Han, Q. L., Chen, C., Wen, S., & Xiang, Y. (2021). Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica, 9(3), 377-391. [Google Scholar] [Crossref]

18. Jahwar, A. F., & Ameen, S. Y. (2021). A review on cybersecurity based on machine learning and deep learning algorithms. Journal of Soft Computing and Data Mining, 2(2), 14-25. [Google Scholar] [Crossref]

19. Nguyen, T. T., & Reddi, V. J. (2021). Deep reinforcement learning for cyber security. IEEE Transactions on Neural Networks and Learning Systems, 34(8), 3779-3795. [Google Scholar] [Crossref]

20. Li, G., Sharma, P., Pan, L., Rajasegarar, S., Karmakar, C., & Patterson, N. (2021). Deep learning algorithms for cyber security applications: A survey. Journal of Computer Security, 29(5), 447-471. [Google Scholar] [Crossref]

21. Olanrewaju-George, B., & Pranggono, B. (2025). Federated learning-based intrusion detection system for the Internet of Things using unsupervised and supervised deep learning models. Cyber Security and Applications, 3, 100068. [Google Scholar] [Crossref]

22. Markkandeyan, S., Ananth, A. D., Rajakumaran, M., Gokila, R. G., Venkatesan, R., & Lakshmi, B. (2025). Novel hybrid deep learning-based cyber security threat detection model with optimization algorithm. Cyber Security and Applications, 3, 100075. [Google Scholar] [Crossref]

23. Imtiaz, N., et al. (2025). A deep learning-based approach for the detection of various Internet of Things intrusion attacks through optical networks. Photonics, 12(35), 1–39. [Google Scholar] [Crossref]

24. Hossain, S., Senouci, S. M., Brik, B., & Boualouache, A. (2025). A privacy-preserving self-supervised learning-based intrusion detection system for 5G-V2X networks. Ad Hoc Networks, 166, 103674. [Google Scholar] [Crossref]

25. Dash, N., Chakravarty, S., Rath, A. K., Giri, N. C., AboRas, K. M., & Gowtham, N. (2025). An optimized LSTM-based deep learning model for anomaly network intrusion detection. Scientific Reports, 15(1), 1554. [Google Scholar] [Crossref]

26. Guo, Z. (2025). Blockchain-enhanced smart contracts for formal verification of IoT access control mechanisms. Alexandria Engineering Journal, 118, 315–324. [Google Scholar] [Crossref]

27. Sriram, S., Tharaniesh, P. R., Saraf, P., Vijayaraj, N., & Murugan, T. (2025). Enhancing digital identity and access control in event management systems using Sui blockchain. IEEE Access. [Google Scholar] [Crossref]

28. Viji, C., Jagannathan, J., Rajkumar, N., Mohanraj, A., Nachiappan, B., & Kovilpillai, J. A. J. (2025). Leveraging blockchain technology to enhance library security. In Enhancing Security and Regulations in Libraries with Blockchain Technology (pp. 181–200). IGI Global. [Google Scholar] [Crossref]

29. Abdellatif, A. A., Shaban, K., & Massoud, A. (2025). Blockchain-enabled distributed learning for enhanced smart grid security and efficiency. Computers and Electrical Engineering, 123, 110012. [Google Scholar] [Crossref]

30. Mohajan, A., & Jahan, S. (2025). Embedding security awareness into a blockchain-based dynamic access control framework for the zero trust model in distributed systems. [Google Scholar] [Crossref]

31. Arif, H., Kumar, A., Fahad, M., & Hussain, H. K. (2024). Future horizons: AI-enhanced threat detection in cloud environments—Unveiling opportunities for research. International Journal of Multidisciplinary Sciences and Arts, 3(1), 242–251. [Google Scholar] [Crossref]

32. Ofoegbu, K. D. O., Osundare, O. S., Ike, C. S., Fakeyede, O. G., & Ige, A. B. (2024). Real-time cybersecurity threat detection using machine learning and big data analytics: [Google Scholar] [Crossref]

33. Olabanji, S. O., Marquis, Y., Adigwe, C. S., Ajayi, S. A., Oladoyinbo, T. O., & Olaniyi, O. O. (2024). AI-driven cloud security: Examining the impact of user behavior analysis on threat detection. Asian Journal of Research in Computer Science, 17(3), 57–74. [Google Scholar] [Crossref]

34. Labu, M. R., & Ahammed, M. F. (2024). Next-generation cyber threat detection and mitigation strategies: A focus on artificial intelligence and machine learning. Journal of Computer Science and Technology Studies, 6(1), 179–188. [Google Scholar] [Crossref]

35. Ismail, S., Nouman, M., Dawoud, D. W., & Reza, H. (2024). Towards a lightweight security framework using blockchain and machine learning. Blockchain: Research and Applications, 5(1), 100174. [Google Scholar] [Crossref]

• “Next-Generation Cybersecurity Through Blockchain and AI Synergy: A Paradigm Shift in Intelligent Threat Mitigation and Decentralised Security”
• Forensic Payroll Analytics for IPPIS: A Hybrid Anomaly-Detection Framework to Expose Payroll Fraud, Improve Data Governance, and Protect Employee Rights
• Factors Influencing Data Protection on Global Trade
• Development Of Artificial Intelligence-Based Model for Forensic Analysis of Cross-Platform Deepfakes
• Cyber Threats and Nigeria’s National Security: Assessing the Role of Regional Cooperation in West Africa