Review on AI-Driven Cyber Deception

In today's cybersecurity landscape, attackers continually evolve their techniques to breach systems, necessitating advanced defenses beyond traditional methods. TrapNet AI is an AI-driven cyber deception system designed to detect and mislead attackers using honeypot technology. The project targets three specific attack vectors: brute force attacks, port scanning, and SQL injection. By deploying AI models, the system analyzes traffic patterns, login attempts, and SQL queries to detect potential threats in real-time. Once an attack is detected, the system employs deception strategies, such as simulating fake vulnerabilities or introducing delays, to confuse and distract the attacker. TrapNet AI is implemented using a React and Tailwind-based frontend for user interaction, while the backend is developed with Node.js and machine learning models, handling detection and deception. The system operates within a local area network, ideal for lab environments, offering a hands-on approach to honeypot simulation. AI algorithms, including supervised and unsupervised models, are employed to distinguish between legitimate and malicious activities, providing a proactive, intelligent defense mechanism.