Strengthening Cloud Security through Machine Learning-Driven Intrusion Detection, Signature Recognition, and Anomaly-Based Threat Detection Systems for Enhanced Protection and Risk Mitigation

Abstract

The increasing complexity and frequency of cyberattacks targeting cloud infrastructures necessitate more advanced security mechanisms beyond conventional rule-based intrusion detection systems, which struggle to keep up with evolving threats. To enhance cloud security, this study proposes a machine learning-driven security framework that integrates intrusion detection, signature-based recognition, and anomaly-based threat detection, ensuring better threat identification and risk mitigation. The approach leverages decision trees, neural networks, and anomaly detection algorithms to distinguish malicious from benign activities, with signature recognition addressing known threats while anomaly detection identifies emerging attack patterns. Experimental evaluations demonstrate that this hybrid system significantly outperforms standalone methods in accuracy, precision, and recall, effectively reducing false positives while improving real-time threat detection. Compared to existing models such as ensemble learning and anomaly-based methods, the integrated framework achieves superior results, with an accuracy of 94.7%, surpassing prior techniques. The findings confirm that combining these methodologies enhances cloud security resilience, with continued model updates and adaptive learning essential for combating evolving cyber threats.