Unauthorized Access Detection System Using Machine Learning with Honeypot Integration

In cases of unauthorized access, this poses a major security risk to the organizational servers. The project is for the development of a high-end detection system by integration of honeypots with machine learning to identify and mitigate unauthorized access attempts in real-time. Honeypots lure attackers to a masqueraded vulnerable system, capturing every detail about the malicious activity. That retrieves detailed information about malicious activities. In this feature extraction for anomaly detection, the features used are sourced from server logs, firewall logs, network traffic data, and authentication logs. This forms the basis for a complete dataset applied in the development of machine learning models with the accuracy of detection of suspicious behavior. All this monitoring is done in real-time, accompanied by automated responses that guarantee correct and timely action execution against detected threats. This is the way the system manages to leverage traditional security measures with the power of innovative honeypot technology to deliver maximum accurate and effective unauthorized access detection, making this an organization's major invention in mission-critical infrastructure security. The approach provides enhanced security and useful insights about attackers' behavior so proactive threat management measures are taken in time.