Digital watermarking, as a technology to protect copyright, integrity, copy prevention or direction tracking of digital products, is currently commonly used to protect confidential documents and files within enterprises. In view of the PDF document format which is widely used in enterprise documents, this paper presents a method of PDF document watermark recognition based on natural language processing technology. By collecting a large number of PDF documents, using the improved N-gram language model based on forward and reverse matching algorithms to segment text content, a KenLM language model based on language model probability and conditional probability calculation rules is established to identify PDF document watermarks, which effectively improves the accuracy of PDF document watermark recognition. The validity of this method is verified by selecting a PDF format document of an enterprise, training the language model and calculating the prediction accuracy.
KEYWORDS: Network security, Information security, Defense and security, Failure analysis, Defense technologies, Security technologies, Network architectures, Internet of things, Detection and tracking algorithms, Computer simulations
As an active defense network security tool, honeypots can lure attacks and capture attack information. However, traditional honeypots can only wait for intrusion passively, and honeypots with network traffic traction function can not realize real-time optimization of diversion strategy based on attack information. Based on the survey of honeypot traceability and current situation of diversion, this paper carries out the research of network traffic traction method based on attack detection and network traffic load balancing. Through five steps of configuring network traffic traction strategy, obtaining attack alarm, comprehensive attack research and judgment, load balancing diversion, and log capture feedback, this method effectively enhances honeypot traceability and improves the level of active network security protection, and introduces the application scenario of this method with portal websites as an example. The effectiveness of the method is tested by comparing various indicators before and after the application of the method.
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