An Adaptive Honeypot-Based Approach for Real-Time Intrusion Detection and System Protection

Abstract

The increasing reliance on interconnected digital systems and internet-enabled infrastructure has significantly increased organizations' exposure to sophisticated cyber threats. Traditional cybersecurity mechanisms such as firewalls, antivirus systems, and signature-based intrusion detection systems are becoming less effective against modern attack techniques, including advanced persistent threats, ransomware, polymorphic malware, and zero-day exploits. Consequently, there is a growing demand for proactive and intelligent cybersecurity solutions that can detect, analyze, and mitigate attacks in real time. This study presents the development of an intelligent honeypot system for real-time cyberattack detection and prevention. The proposed system integrates honeypot technology with intelligent monitoring, behavioral analysis, automated alert generation, and adaptive prevention mechanisms to enhance organizational cybersecurity defense. The system was developed using the Python programming language, Linux server environments, MySQL database systems, and network monitoring tools. Experimental evaluations involving simulated attacks such as brute-force login attempts, port scanning, malware injection, and denial-of-service attacks demonstrated that the system effectively detects malicious activities, generates real-time alerts, and provides valuable threat intelligence for security administrators. The findings indicate that intelligent honeypot systems significantly improve proactive cyber defense capabilities by enhancing attack visibility, reducing response time, and supporting real-time threat mitigation.

Keywords