Enhancing Cloud Security Using Generative AI for Intrusion Detection

DOI

10.2991/978-94-6463-872-1_50How to use a DOI?

Keywords

Cloud Security; Intrusion detection; Generative AI; Cyber-attacks; ToN-IoT; Denial-of-service (DoS)

Abstract

Cloud computing has revolutionized data processing, while it is still quite fairly vulnerable to advanced cyberattacks, including DDoS attacks and zero-day flaws. Regarding changing assault methods, data imbalance, and real-time threat detection, traditional intrusion detection systems (IDSs) struggle. This paper presents a Generative AI-enhanced Intrusion Detection System (GAI-IDS) to improve threat detection accuracy and resilience by combining Transformer-based anomaly detection with Conditional Generative Adversarial Networks (CGANs). The model balances insufficiently represented threat classes, provides reasonable synthetic attack samples, and uses a multi-head self-attention technique for real- time anomaly detection. With a 96.5% detection accuracy, experimental results on the ToN-IoT dataset much exceed conventional IDS. Moreover, adversarial training builds up the system to resist evasive cyberattacks, providing scalable and flexible cloud security.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vineet Kumar Srivastava
AU  - Vishnu Ravi
AU  - Maninder Pal Singh
AU  - Nuzhat Noor Islam Prova
PY  - 2025
DA  - 2025/11/04
TI  - Enhancing Cloud Security Using Generative AI for Intrusion Detection
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 810
EP  - 824
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_50
DO  - 10.2991/978-94-6463-872-1_50
ID  - Srivastava2025
ER  -