{"product_id":"9783031874956","title":"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","description":"\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThe 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:\u003c\/p\u003e\nPart 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\ninfrastructure; 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. \u003cp\u003e.- \u003cstrong\u003ePreserving privacy and maintaining trust for end users in a complex and numeric cyberspace.\u003c\/strong\u003e\n.- Another Walk for Monchi.\n.- An Innovative DSSE Framework: Ensuring Data Privacy and Query Verification in Untrusted Cloud Environments.\n.- Privacy-Preserving Machine Learning Inference for Intrusion Detection.\n.- Priv-IoT: Privacy-preserving Machine Learning in IoT Utilizing TEE and Lightweight Ciphers.\n.- Intersecting security, privacy, and machine learning techniques to detect, mitigate, and prevent threats.\n.- \u003cstrong\u003eLocalIntel: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge.\u003c\/strong\u003e\n.- Intelligent Green Efficiency for Intrusion Detection.\n.- A Privacy-Preserving Behavioral Authentication System.\n.- Automated Exploration of Optimal Neural Network Structures for Deepfake Detection.\n.- An Empirical Study of Black-box based Membership Inference Attacks on a Real-World Dataset.\n.- \u003cstrong\u003eNew trends of machine leaning and AI applied to cybersecurity\u003c\/strong\u003e.\n.- ModelForge: Using GenAI to Improve the Development of Security Protocols.\n.- Detecting Energy Attacks in the Battery-less Internet of Things .\n.- Is Expert-Labeled Data Worth the Cost? Exploring Active and Semi-Supervised Learning Across Imbalance Scenarios in Financial Crime Detection.\n.- ExploitabilityBirthMark: An Early Predictor of the Likelihood of Exploitation.\u003c\/p\u003e","brand":"SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING","offers":[{"title":"Default Title","offer_id":48865597292779,"sku":"00000_00000_00000_00000","price":256.34,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9783031874956-1.jpg?v=1781717877","url":"https:\/\/kinokuniya.com.sg\/products\/9783031874956","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}