Leveraging Contractive Autoencoders for Time-Efficient Rare Cyberattack Detection

The rapid adoption of cloud computing has introduced critical security challenges in the cloud, with evolving cyberattacks exposing vulnerabilities in conventional intrusion detection systems (IDS). Existing approaches often struggle with high false-positive rates, poor handling of imbalanced traffic, and computational overhead in dynamic cloud environments. To address these issues, we propose SLCAE-BiLSTM, a deep learning-based IDS which enhances feature extraction and sequential learning. The Single-Layer Contractive Autoencoder (SLCAE) ensures efficient data representation by minimizing redundancy while preserving critical attack patterns. Meanwhile, the Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies in network traffic, improving the detection of rare attacks. Experimental evaluations on two benchmark datasets demonstrate SLCAE-BiLSTM's superiority, achieving 99.91% and 99.87% accuracy in binary classification and 97.73% and 91.22% in multi-class classification, surpassing state-of-theart models such as SCAE-SVM, SAE-SVM, and SDAE-SVM. These high accuracy rates indicate a significant reduction in misclassification and improved detection of both common and rare cyber threats. Furthermore, its reduced computational overhead and faster inference time makes it an efficient solution for enhancing cloud security against emerging threats.