Authors - Madhumati Pol, Rutuja Chaudhari, Sai Jadhav, Sri Sai Preethi Munnaluri, Rudrani Sarangdhar Abstract - The necessity of developing adaptive, autonomous, and intelligent security systems has developed significantly over time because of the increased volume and complexity of cyber attacks. Therefore, this research project will present an AI-powered multi agent self-evolution cybersecurity intelligence system. The purpose of the system will be for the real-time identification, classification, and prediction of cyber threats. The system will consist of three working agents: Network Monitoring Agent, System-Metrics Surveillance Agent and Threat Intelligence Agent. These agents will be supported by interpretable machine learning classifiers and light-weight Python-based data collection tools. A universal dataset converter will enable it to operate on all types of cybersecurity datasets, and a self-evolving element will allow it to continually update itself with additional information regarding current threats. Dashboards will be provided through the use of Streamlit in order to provide real-time timelines of attacks, CVE intelligence, anomaly detection, and real-time visualization of threats. Results from experimental testing show that the system can improve the accuracy of its threat detection as time progresses and perform well across various datasets. Overall, this work provides a self-learning, scalable, modular, and dataset-agnostic architecture for use within modern enterprise-level cybersecurity environments.
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