Student Publications [Scholarly]

Dual-Encoder Adversarial Learning for Cloud-Based Cyber Intrusion Detection

Document Type

Article

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.

Publication Title

IEEE Open Journal of the Computer Society

Publication Date

2025

ISSN

2644-1268

DOI

10.1109/OJCS.2025.3637858

Keywords

adversarial learning, anomaly detection, cloud security, cybersecurity, deep learning, domain adaptation, intrusion detection system, shared-private encoder

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