An Adaptive Data-Driven Framework for Real-Time Cyber Threat Detection and Response

Abstract The conventional model of cybersecurity, founded on fixed sets of rules and labelled sets, demonstrates quite limited performance in the face of the uncertainty of zero-day attacks and operational imperatives introduced by high false-positive rates and slow reactions. Contrarily, the RTACD model is a major improvement. RTACD, which is based on the supervised, semi-supervised, and unsupervised machine learning, allows detecting the threats immediately and responding to them dynamically and contextually. Notably, compared to the earlier models, which heavily depended on the presence of pre-existing threat signatures or large volumes of pre-collected labelled data, RTACD constantly learns on the latest data streams and uses reinforcement learning to tighten the screws. The preliminary tests, such as testing on the CICIDS2017 dataset, and real-world deployments to healthcare, finance, and government, establish the following improvements: false positives have been reduced by 30%, ransomware cases by 75%, and the accuracy of existing threats is above 98\% and that of the zero-day anomalies above 92%. The gist is that RTACD framework presents a scalable and dynamic solution that improves the resilience of organizations and the continuity of their operations and closes long-term vulnerabilities of conventional approaches to cybersecurity.