A Hybrid BiLSTM-Attention and Homomorphic Encryption Framework for Privacy-Preserving Threat Detection in Cloud Computing Environments
Narsing Rao Dyavani, Venkat Garikipati, Charles Ubagaram, Bhagath Singh Jayaprakasam, Rohith Reddy Mandala, R. Aiswarya · SPIN · 2025
Cloud computing has become indispensable for modern digital services in healthcare, finance and education, yet its distributed and multi-tenant nature exposes it to increasingly sophisticated cyberattacks. With the anticipated integration of quantum computing into cloud infrastructures, the need for robust, privacy-preserving and fault-tolerant security mechanisms is greater than ever. Traditional threat detection models, including Convolutional Neural Networks, Support Vector Machines and Autoencoders, rely on raw or partially anonymized data, which risks user privacy and lacks the ability to effectively capture sequential dependencies in dynamic cloud traffic. To address these limitations, we propose a Hybrid BiLSTM-Attention and Homomorphic Encryption Framework for intrusion detection in cloud environments. The BiLSTM component extracts bidirectional temporal dependencies from encrypted traffic, while the attention mechanism highlights critical time steps to improve detection accuracy. Homomorphic Encryption ensures sensitive data remains encrypted throughout the detection pipeline, supporting privacy without sacrificing computational performance. Experimental results on a benchmark intrusion detection dataset demonstrate superior performance, achieving 99.28% accuracy, 99.26% precision, 99.89% recall and a 99.5% F1-score, along with a low false-negative rate (0.1%) and high AUC (0.9847). Training (1.2[Formula: see text]s) and prediction (0.2[Formula: see text]s) times confirm the framework’s suitability for real-time deployment in privacy-critical applications. Importantly, the proposed system embodies principles of fault tolerance by maintaining resilience against adversarial interference and data leakage, a requirement that closely parallels the reliability demands of emerging quantum-classical hybrid systems. This synergy between deep learning, encryption and fault tolerance provides a pathway toward secure and privacy-preserving cloud infrastructures in the quantum computing era.