Abstract: With the development of smart cities and public security big data,video surveillance networks have become essential infrastructure for urban governance.However,by replacing or tampering with surveillance cameras- the important front-end device,an attacker can access the internal network to achieve device hijacking,information theft,network paralysis,and threatening personal,social,and national security.A camera identity recognition method combining multi-dimensional identification features is proposed to identify illegal camera identities in advance.A camera identification system that integrates explicit,implicit,and dynamic identifiers is constructed by extracting the camera's static information and the dynamic flow information.An evaluation method of identifier contribution based on self-information and information entropy is proposed to select a concise and practical identity identifier.The extracted identifier feature vector can lay the foundation for future abnormal camera intrusion detection.Experimental results show that explicit identifiers have the most considerable amount of self-information and contribution but are easy to be forged;dynamic identifiers have the second-highest contribution,but the workload of traffic collection and processing is enormous;static identifiers have a low contribution but still have a specific role in identification.

Key words: Camera, Identification feature, Identity recognition, Intrusion detection, Self-information amount