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Network and System Security = 14th I...
~
Zhang, Jun.
Network and System Security = 14th International Conference, NSS 2020, Melbourne, VIC, Australia, November 25–27, 2020, Proceedings /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Network and System Security/ edited by Mirosław Kutyłowski, Jun Zhang, Chao Chen.
其他題名:
14th International Conference, NSS 2020, Melbourne, VIC, Australia, November 25–27, 2020, Proceedings /
其他作者:
Chen, Chao.
面頁冊數:
XIII, 448 p. 135 illus., 103 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Mobile and Network Security. -
電子資源:
https://doi.org/10.1007/978-3-030-65745-1
ISBN:
9783030657451
Network and System Security = 14th International Conference, NSS 2020, Melbourne, VIC, Australia, November 25–27, 2020, Proceedings /
Network and System Security
14th International Conference, NSS 2020, Melbourne, VIC, Australia, November 25–27, 2020, Proceedings /[electronic resource] :edited by Mirosław Kutyłowski, Jun Zhang, Chao Chen. - 1st ed. 2020. - XIII, 448 p. 135 illus., 103 illus. in color.online resource. - Security and Cryptology ;12570. - Security and Cryptology ;9241.
Data Analytics of Crowdsourced Resources for Cybersecurity Intelligence -- Security Evaluation of Smart Contract-Based On-Chain Ethereum Wallets -- EnCoD Distinguishing Compressed and Encrypted File Fragments -- A Weighted Voting Framework for Android App’s Vetting Based on Multiple Machine Learning Models -- HyperWall A Hypervisor for Detection and Prevention of Malicious Communication -- Safety Analysis of High-Dimensional Anonymized Data from Multiple Perspectives -- Defending Against Package Typosquatting -- Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network -- Game Theoretic Analysis of Reputation Approach on Block Withholding Attack -- Identity-Based Outsider Anonymous Broadcast Encryption with Simultaneous Individual Messaging -- Compactly Committing Authenticated Encryption Using Tweakable Block Cipher -- Model Poisoning Defense on Federated Learning: A Validation Based Approach -- Fixing Vulnerabilities Automatically with Linters -- Sequential Anomaly Detection Using Feedback and Prioritized Experience Replay -- A Framework of Blockchain-Based Collaborative Intrusion Detection in Software Defined Networking -- Evaluation of Anomaly-Based Intrusion Detection with Combined Imbalance Correction and Feature Selection -- AC0 Constructions of Secret Sharing Schemes – Accommodating New Parties -- Multiply, Divide, and Conquer – Making Fully Decentralised Access Control a Reality -- A Model Specification Implementation for Trust Negotiation -- Privacy and Utility Trade-off for Textual Analysis via Calibrated Multivariate Perturbations -- PowerKey: Generating Secret Keys from Power Line Electromagnetic Interferences -- On the Vulnerability of Hyperdimensional Computing-Based Classifiers to Adversarial Attacks -- ESCAPADE: Encryption-Type-Ransomware: System Call Based Pattern Detection -- A Privacy Preserving Aggregation Scheme for Fog-Based Recommender System -- The Impact of Differential Privacy on Model Fairness in Federated Learning -- Evading Stepping-Stone Detection with Enough Chaff.
This book constitutes the refereed proceedings of the 14th International Conference on Network and System Security, NSS 2020, held in Melbourne, VIC, Australia, in November 2020. The 17 full and 9 short papers were carefully reviewed and selected from 60 submissions. The selected papers are devoted to topics such as secure operating system architectures, applications programming and security testing, intrusion and attack detection, cybersecurity intelligence, access control, cryptographic techniques, cryptocurrencies, ransomware, anonymity, trust, recommendation systems, as well machine learning problems. Due to the Corona pandemic the event was held virtually.
ISBN: 9783030657451
Standard No.: 10.1007/978-3-030-65745-1doiSubjects--Topical Terms:
1211619
Mobile and Network Security.
LC Class. No.: QA76.9.D335
Dewey Class. No.: 005.82
Network and System Security = 14th International Conference, NSS 2020, Melbourne, VIC, Australia, November 25–27, 2020, Proceedings /
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Data Analytics of Crowdsourced Resources for Cybersecurity Intelligence -- Security Evaluation of Smart Contract-Based On-Chain Ethereum Wallets -- EnCoD Distinguishing Compressed and Encrypted File Fragments -- A Weighted Voting Framework for Android App’s Vetting Based on Multiple Machine Learning Models -- HyperWall A Hypervisor for Detection and Prevention of Malicious Communication -- Safety Analysis of High-Dimensional Anonymized Data from Multiple Perspectives -- Defending Against Package Typosquatting -- Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network -- Game Theoretic Analysis of Reputation Approach on Block Withholding Attack -- Identity-Based Outsider Anonymous Broadcast Encryption with Simultaneous Individual Messaging -- Compactly Committing Authenticated Encryption Using Tweakable Block Cipher -- Model Poisoning Defense on Federated Learning: A Validation Based Approach -- Fixing Vulnerabilities Automatically with Linters -- Sequential Anomaly Detection Using Feedback and Prioritized Experience Replay -- A Framework of Blockchain-Based Collaborative Intrusion Detection in Software Defined Networking -- Evaluation of Anomaly-Based Intrusion Detection with Combined Imbalance Correction and Feature Selection -- AC0 Constructions of Secret Sharing Schemes – Accommodating New Parties -- Multiply, Divide, and Conquer – Making Fully Decentralised Access Control a Reality -- A Model Specification Implementation for Trust Negotiation -- Privacy and Utility Trade-off for Textual Analysis via Calibrated Multivariate Perturbations -- PowerKey: Generating Secret Keys from Power Line Electromagnetic Interferences -- On the Vulnerability of Hyperdimensional Computing-Based Classifiers to Adversarial Attacks -- ESCAPADE: Encryption-Type-Ransomware: System Call Based Pattern Detection -- A Privacy Preserving Aggregation Scheme for Fog-Based Recommender System -- The Impact of Differential Privacy on Model Fairness in Federated Learning -- Evading Stepping-Stone Detection with Enough Chaff.
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