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Titlebook: Artificial Intelligence and Security; 5th International Co Xingming Sun,Zhaoqing Pan,Elisa Bertino Conference proceedings 2019 Springer Nat

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期刊全称Artificial Intelligence and Security
期刊简称5th International Co
影响因子2023Xingming Sun,Zhaoqing Pan,Elisa Bertino
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Artificial Intelligence and Security; 5th International Co Xingming Sun,Zhaoqing Pan,Elisa Bertino Conference proceedings 2019 Springer Nat
影响因子The 4-volume set LNCS 11632 until LNCS 11635 constitutes the refereed proceedings of the 5.th. International Conference on Artificial Intelligence and Security, ICAIS 2019, which was held in New York, USA, in July 2019. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS..The total of 230 full papers presented in this 4-volume proceedings was carefully reviewed and selected from 1529 submissions. The papers were organized in topical sections as follows:..Part I: cloud computing;. Part II: artificial intelligence; big data; and cloud computing and security;. Part III: cloud computing and security; information hiding; IoT security; multimedia forensics; and encryption and cybersecurity;. Part IV: encryption and cybersecurity..
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A Secure Data Aggregation Protocol in VANETs Based on Multi-key FHEd decryption, and then the partial sharing decryption is proposed. Performance analysis illustrated that the proposed scheme is feasible and the complexity expands. Under the universal composability frame, the proposed protocol is also proved to be semantically secure.
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Conference proceedings 20199 submissions. The papers were organized in topical sections as follows:..Part I: cloud computing;. Part II: artificial intelligence; big data; and cloud computing and security;. Part III: cloud computing and security; information hiding; IoT security; multimedia forensics; and encryption and cybersecurity;. Part IV: encryption and cybersecurity..
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https://doi.org/10.1007/1-56898-635-1cture of the application. The samples with low-frequency path are preferably selected during the sample selection phase using simulated annealing and genetic algorithms. The experimental results on the LAVA-M dataset justify the better performance of the proposed GVDM model, which finds more vulnerabilities than other fuzzers with high accuracy.
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