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Record Nr. |
UNINA9910409661403321 |
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Titolo |
Cyber Security Cryptography and Machine Learning : Fourth International Symposium, CSCML 2020, Be'er Sheva, Israel, July 2–3, 2020, Proceedings / / edited by Shlomi Dolev, Vladimir Kolesnikov, Sachin Lodha, Gera Weiss |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (265 pages) : illustrations |
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Collana |
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Security and Cryptology ; ; 12161 |
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Disciplina |
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Soggetti |
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Computer security |
Computer communication systems |
Data encryption (Computer science) |
Computers |
Architecture, Computer |
Computer networks - Security measures |
Systems and Data Security |
Computer Communication Networks |
Cryptology |
Information Systems and Communication Service |
Computer System Implementation |
Mobile and Network Security |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Single Tweakey Cryptanalysis of Reduced-Round Skinny-64 -- Zero-Knowledge to the Rescue: Consistent Redundant Backup of Keys Generated for Critical Financial Services -- Security Ranking of IoT Devices Using an AHP Model -- Robust Malicious Domain Detection -- NeuroGIFT : Using a Machine Learning Based Sat Solver for Cryptanalysis -- Can the Operator of a Drone be Located by Following the Drone's Path? -- Detecting Malicious Accounts on the Ethereum Blockchain with Supervised Learning -- Fast Polynomial Inversion for |
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Post Quantum QC-MDPC Cryptography -- Efficient CORDIC-based Sine and Cosine Implementation for a Datafow Architecture -- SecureMCMR: Computation Outsourcing for MapReduce Applications -- Evasion is not enough: A Case Study of Android Malware -- Toward Self-Stabilizing Blockchain, Reconstructing Totally Erased Blockchain -- A Recommender System for Efficient Implementation of Privacy Preserving Machine Learning Primitives based on FHE -- Comparison of DNS based Methods for Detecting Malicious Domains -- Average-case Competitive Ratio for Evaluating Scheduling Algorithms of Multi-user Cache -- CryptoRNN - Privacy-Preserving Recurrent Neural Networks using Homomorphic Encryption. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the Fourth International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020, held in Beer-Sheva, Israel, in July 2020. The 12 full and 4 short papers presented in this volume were carefully reviewed and selected from 38 submissions. They deal with the theory, design, analysis, implementation, or application of cyber security, cryptography and machine learning systems and networks, and conceptually innovative topics in these research areas. |
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