Real-Time Cloud Intrusion Detection with SpinalSAENet: A Sparse Autoencoder Approach with Focal Loss Optimization

Abstract

The swift growth of cloud computing has heightened cybersecurity vulnerabilities, demanding robust intrusion detection systems (IDS). Conventional IDS models face challenges, such as excessive false positives and limited flexibility. This study introduces Spinal Stacked AutoEncoder Net (SpinalSAENet), an innovative hybrid deep-learning-based IDS that merges SpinalNet and Deep Stacked AutoEncoders (DSAE) to enhance anomaly detection and data integrity verification. The system employs feature extraction and Chebyshev distance-based fusion to improve classification, while Principal Component Analysis (PCA) is utilised to reduce dimensionality, thereby increasing computational efficiency. When tested on the Bot-IoT dataset, SpinalSAENet demonstrated superior performance with 96.87% accuracy, 95.4% recall, 96.1% precision, and a 95.7% F1-score, surpassing Decision Trees, Random Forests, and Support Vector Machines. The incorporation of SHA-256 hashing and Merkle tree proofs ensures data integrity, offering a multitiered security approach. Its streamlined architecture and cloud-native scalability (Docker and Kubernetes) facilitate real-time deployment in cloud environments. This paper presents a highly precise and scalable IDS framework capable of real-time intrusion detection and data integrity verification. Subsequent research will investigate the resistance to adversarial attacks, explainable AI, and serverless deployment to further enhance cloud security.