Citation

Amin, Md Al and Ahsan, Mohammad Shafat and Islam, Rahomotul and Ahmed, Mumtahina and Mridha, M. F. and Hossen, Md. Jakir (2025) Dual-Encoder Adversarial Learning for Cloud-Based Cyber Intrusion Detection. IEEE Open Journal of the Computer Society. pp. 1-12. ISSN 2644-1268

Text Dual-Encoder Adversarial Learning for Cloud-Based Cyber Intrusion Detection.pdf - Published Version Restricted to Repository staff only Download (843kB)

Abstract

Cloud-based systems have become prime targets for increasingly sophisticated cyberattacks due to their distributed, multi-tenant, and dynamic nature. Traditional intrusion detection systems often fail to generalize across diverse cloud environments, particularly when dealing with heterogeneous data sources such as network traffic and user behavior. In this paper, we propose a novel Dual-Encoder Adversarial Learning framework designed to detect cyber intrusions in cloud systems through a unified representation of multi-domain features. The architecture integrates private encoders for domain-specific signal extraction, a shared encoder for domain-invariant representation learning, and adversarial training implemented via a Gradient Reversal Layer to enforce cross-domain generalization. A reconstruction decoder is also employed to retain semantic fidelity in latent embeddings. Extensive experiments conducted on two benchmark datasets, UNSW-NB15 and the Cybersecurity Intrusion Detection dataset, demonstrate that the proposed model consistently outperforms state-of-the-art baselines in terms of accuracy (0.917), precision (0.903), recall (0.910), F1-score (0.906), and AUC (0.947). The model also exhibits strong robustness under noisy conditions and achieves competitive efficiency in training time and memory usage. These results validate the effectiveness of the proposed architecture for efficient and robust intrusion detection in cloud environments.

Item Type:	Article
Uncontrolled Keywords:	Cybersecurity, deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Dec 2025 03:46
Last Modified:	22 Dec 2025 03:46
URII:	http://shdl.mmu.edu.my/id/eprint/15094

Downloads

Edit (login required)