Developing an Adaptive Security Framework for Real-Time Threat Detection and Response in Cloud-Network Systems
J Paramesh, K.P. Sriram, E. Anbalagan, S. Sasikumar, M. Guru Vimal Kumar · 2024
The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed; conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts.