Intrusion detection with Quasi-Recurrent Scaled Residual Networks: a deep learning approach for cloud security

Cloud computing plays a vital role in providing data access and storage services, but it is vulnerable to malicious attacks and unauthorized access, compromising data confidentiality and integrity. Intrusion Detection Systems (IDS) are essential for detecting such threats; however, existing IDSs face challenges in handling large volumes of cloud traffic and often suffer from low accuracy and false negatives. The primary objective of this research is to develop a robust and accurate intrusion detection system called Quasi Recurrent Scaled Residual Network (QRSRes-Net) for cloud intrusion detection. Input network traffic is preprocessed using sigmoid normalization and missing value restoration, followed by Topsoe correlation (TopCorr) for feature selection. The Synthetic Minority Oversampling Technique (SMOTE) addresses class imbalance and improves minority attack representation. Intrusion detection is performed using QRSRes-Net, which integrates Scaling Wide Residual Networks (SWideResNet) and Quasi-Recurrent Neural Networks (QRNN) to capture spatial and temporal dependencies effectively. The model is trained and evaluated on the publicly available CIC QNSL dataset, containing diverse cloud traffic and multiple attack types. The proposed approach achieves 92.445% accuracy, 92.997% TPR, 92.309% TNR, 92.009% precision, and 92.500% F1-score, demonstrating its effectiveness and robustness for large-scale cloud intrusion detection.