AI-enhanced cloud security monitoring: Detecting advanced persistent threats and intrusions using deep autoencoders and hybrid machine learning techniques

Cloud computing is slowly becoming one of the main infrastructures for businesses, putting it at risk to undergo Advanced Persistent Threats (APTs) and advanced cyberattacks. Traditional intrusion detection systems (IDS) use rule-based or signature-based techniques, which cannot identify zero-day attacks and evolving threats since they solely depend on predefined attacks' signatures. This study proposes an AI-enhanced continuous security monitoring system that combines deep autoencoders for anomaly detection with a hybrid model, MLP-GRU, for threat classification. The deep autoencoder accurately learns network activity and detects deviations, while the MLP-GRU model analyses sequential data patterns, which leads to the increase in classification accuracy. Experimental results using key performance metrics of accuracy, precision, recall, F1-score, and AUC-ROC confirm the efficiency of the proposed system, ensuring its success in differentiating normal from harmful activity. Besides, the throughput analysis demonstrates that it functions in real time to take care of security events within the system. The proposed methodology serves as a viable alternative to conventional IDSs, enhancing the scalability, adaptability, and accuracy of malware detection. Conclusively, future research will focus on adaptive learning, federated security monitoring, and explainable AI towards realizing enhanced detection capabilities.