Foundations and Practice of Security : 17th International Symposium, FPS 2024, Montréal, QC, Canada, December 9-11, 2024, Revised Selected Papers, Part II (Lecture Notes in Computer Science 15533) (2025. xv, 200 S. XV, 200 p. 52 illus., 48 illus. in color. 235 mm)

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Foundations and Practice of Security : 17th International Symposium, FPS 2024, Montréal, QC, Canada, December 9-11, 2024, Revised Selected Papers, Part II (Lecture Notes in Computer Science 15533) (2025. xv, 200 S. XV, 200 p. 52 illus., 48 illus. in color. 235 mm)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 175 p.
  • 言語 ENG
  • 商品コード 9783031874956

Full Description

This two-volume set constitutes the refereed proceedings of the 17th International Symposium on Foundations and Practice of Security, FPS 2024, held in Montréal, QC, Canada, during December 09-11, 2024.

The 28 full and 11 short papers presented in this book were carefully reviewed and selected from 75 submissions. The papers were organized in the following topical sections:

Part I: Critical issues of protecting systems against digital threats,considering financial, technological, and operational implications; Automating and enhancing security mechanisms in software systems and data management; Cybersecurity and AI when applied to emerging technologies; Cybersecurity and Ethics; Cybersecurity and privacy in connected and autonomous systems for IoT, smart environments, and critical
infrastructure; New trends in advanced cryptographic protocols.

 
Part II: Preserving privacy and maintaining trust for end users in a complex and numeric cyberspace; Intersecting security, privacy, and machine learning techniques to detect, mitigate, and prevent threats; New trends of machine leaning and AI applied to cybersecurity.

Contents

.- Preserving privacy and maintaining trust for end users in a complex and numeric cyberspace.
.- Another Walk for Monchi.
.- An Innovative DSSE Framework: Ensuring Data Privacy and Query Verification in Untrusted Cloud Environments.
.- Privacy-Preserving Machine Learning Inference for Intrusion Detection.
.- Priv-IoT: Privacy-preserving Machine Learning in IoT Utilizing TEE and Lightweight Ciphers.
.- Intersecting security, privacy, and machine learning techniques to detect, mitigate, and prevent threats.
.- LocalIntel: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge.
.- Intelligent Green Efficiency for Intrusion Detection.
.- A Privacy-Preserving Behavioral Authentication System.
.- Automated Exploration of Optimal Neural Network Structures for Deepfake Detection.
.- An Empirical Study of Black-box based Membership Inference Attacks on a Real-World Dataset.
.- New trends of machine leaning and AI applied to cybersecurity.
.- ModelForge: Using GenAI to Improve the Development of Security Protocols.
.- Detecting Energy Attacks in the Battery-less Internet of Things .
.- Is Expert-Labeled Data Worth the Cost? Exploring Active and Semi-Supervised Learning Across Imbalance Scenarios in Financial Crime Detection.
.- ExploitabilityBirthMark: An Early Predictor of the Likelihood of Exploitation.

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