Deep Learning Powered Adversarial Sample Attack Approach for Security Detection of DGA Domain Name in Cyber Physical Systems

With the development of wireless communication, cyber physical system (CPS) technologies are being applied to various fields, and people's daily lives are more dependent on CPS. As CPS brings convenience to people's lives, danger also arises. The most serious of these is attacks on CPS. Attackers obtain information without the user's permission. The main transmission medium used by attackers is the botnet. The domain generation algorithm is mainly used in botnets. This algorithm generates and registers a large number of domain names in a very short time for CPS, and then binds the IP address of the botnet controller. Due to the development of domain generation methods, the detection of such domains is crucial for security in CPS but has stagnated. To end the situation, this article proposes a domain name detection system to solve this security issue in CPS. In the system, a deep learning powered adversarial sample attacks approach is embedded to improve its performance. Through experiments, the proposed system achieves better performance in malicious domain name recognition.