A new dynamic security defense system based on TCP_REPAIR and deep learning

Honeypot is an active defense mechanism, which attracts attackers to interact with virtual resources in the honeypot mainly by simulating real working scenarios and deploying decoy targets, so as to prevent real resources from being damaged and collect attackers’ attack processes and analyze potential system vulnerabilities to proactively respond to similar attacks. Because of the existing honeypot system has defects such as the inability to deploy specific honeypots to induce attacks based on complex attacks, the inability to select the best honeypot for dynamic response based on honeypot deployment and maintenance costs during attack interactions, and insufficient ability to identify variants of known attack methods. Although hybrid honeypots can solve some of these problems by deploying low-interaction honeypots and high-interaction honeypots, they cannot really be applied to real production scenarios because of their slow TCP connection switching speed and inability to efficiently identify encrypted malicious traffic. In this paper, we propose a new dynamic security defense system based on the combination of TCP_REPAIR-based dynamic honeypot selection architecture and a deep learning-based intelligent firewall. The system accurately distributes encrypted or non-encrypted attack traffic and its variants through the intelligent firewall. The normal traffic is sent to the actual system, and the marked malicious traffic dynamically selects honeypots to respond according to the attack process.The experimental result indicated that the system can select honeypots for targeted responses according to the actual network situation quickly and dynamically and covertly, effectively improving the utilization rate of honeypot clusters as well as the ability to decoy.