Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3-4, 2024, London, UK

The C3AI 2024
Herausgegeben:Naik, Nitin; Jenkins, Paul; Prajapat, Shaligram; Grace, Paul

• Broschiertes Buch

Produktbeschreibung

This book offers an in-depth exploration of cutting-edge research across the interconnected fields of computing, communication, cybersecurity, and artificial intelligence. It serves as a comprehensive guide to the technologies shaping our digital world, providing both a profound understanding of these domains and practical strategies for addressing their challenges. The content is drawn from the International Conference on Computing, Communication, Cybersecurity and AI (C3AI 2024), held in London, UK, from July 3 to 4, 2024. The conference attracted 66 submissions from 17 countries, including the USA, UK, Canada, Brazil, India, China, Germany, and Spain. Of these, 47 high-calibre papers were rigorously selected through a meticulous review process, where each paper received three to four reviews to ensure quality and relevance. This book is an essential resource for readers seeking a thorough and timely review of the latest advancements and trends in computing, communication, cybersecurity, and artificial intelligence.

Produktdetails

• Lecture Notes in Networks and Systems 884
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-74442-6
• Seitenzahl: 845
• Erscheinungstermin: November 2024
• Englisch
• Abmessung: 235mm x 155mm
• Gewicht: 1241g
• ISBN-13: 9783031744426
• Artikelnr.: 71521031

Inhaltsangabe

Security model for IoT applications IoTSeMo.- DAN Deep Neural Network-based Application Mapping for Optimized Network-on-Chip Design.- Threat Modelling in Virtual Assistant Hub Devices.- Generate Unnoticeable Adversarial Examples on Audio Classification Models with Multi perspective Objectives.- Prior enhanced Semi supervised Federated Learning for IoT Intrusion Detection A Game Theory and Comparative Learning based Approach.- An empirical study on Insider Threats Towards Crime Prevention through Environmental Design CPTED A student case study.- Utilizing Machine Learning and Deep Learning Techniques for the Detection of Distributed Denial of Service DDoS Attacks.- Inspecting software architecture design styles to infer threat models and inform likely attacks.