Biblio

With the rapid development of artificial intelligence technology, deepfake technology based on deep learning is receiving more and more attention from society or the industry. While enriching people's cultural and entertainment life, in-depth fakes technology has also caused many social problems, especially potential risks to managing network credible identities. With the continuous advancement of deep fakes technology, the security threats and trust crisis caused by it will become more serious. It is urgent to take adequate measures to curb the abuse risk of deep fakes. The article first introduces the principles and characteristics of deep fakes technology and then deeply analyzes its severe challenges to network trusted identity management. Finally, it researches the supervision and technical level and puts forward targeted preventive countermeasures.

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.