DESIGN OF A SECURITY PROTOCOL FOR REAL-TIME INTRUSION DETECTION AND ATTACKER TRACING

The shifting landscape of cyber threats, from unrelenting attacks to advanced malware, has exposed the flaws in each component of older defense methods; traditional tools do not offer real-time detection and identification of attackers. Addressing this critical flaw, this study proposes an innovative security protocol, called TRACE (Tracking and Reporting of Attacks through Cybersecurity Engine), that provides a comprehensive solution facilitating automated reporting, intrusion detection, and attack attribution. TRACE consists of three modules: an attacker fingerprinting module, which utilizes digital evidence to help track and identify attack sources; an AI-driven anomaly engine to continuously scan over traffic flows; and a secure channel to provide timely alerts and accountability. By creating an effective attacker profile as part of the suspicious activity monitoring procedure, TRACE offers superior defensive and forensic alternatives in comparison to conventional Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). TRACE has been thoroughly tested through a series of simulation tests across various network environments, and compared against existing intrusion prevention methodologies to ensure latency shortages are kept to a minimum, false positives are eradicated, and detection is precise. The data confirms TRACE is a next-level, cutting-edge cybersecurity protocol that provided up to X% better accuracy and Y% faster response time in comparison to other systems. By providing a proactive, open, and flexible solution that closes the gap between real-time protection and post-attack accountability, this study contributes to the current conversation on intelligent security frameworks.