Machine Learning for Cyber Security

5th International Conference, ML4CS 2023, Yanuca Island, Fiji, December 4-6, 2023, Proceedings
2024
ISBN: 978-981-97-2457-4
Verlag: Springer Nature Singapore

Buch, Englisch, Band 14541, 174 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 295 g

Reihe: Lecture Notes in Computer Science

This book constitutes the referred proceedings of the 5th International Conference on Machine Learning for Cyber Security, ML4CS 2023, held in Yanuca Island, Fiji, during December 4–6, 2023.

The 11 full papers presented in this book were carefully reviewed and selected from 35 submissions. They cover a variety of topics, including cybersecurity, AI security, machine learning security, encryption, authentication, data security and privacy, cybersecurity forensic analysis, vulnerability analysis, malware analysis, anomaly and intrusion detection.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Chao

Kim, Dan Dongseong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit

Inhaltsverzeichnis

Keystroke Transcription from Acoustic Emanations Using Continuous Wavelet Transform.- Strengthening Cyber Security Education Designing Robust Assessments for Chat GPT Generated Answers.- Pass File Graphical Password Authentication based on File Browsing Records.- On the Role of Similarity in Detecting Masquerading Files.- A Password-based Mutual Authenticated Key Exchange Scheme by Blockchain for WBAN.- Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle Road Collaboration.- A client side watermarking with private class in Federated learning.- Research on Evasion and Detection of Malicious JavaScript Code.- Tackling Non IID for Federated Learning with Components Alignment.- Security on top of Security Detecting Malicious Firewall Policy Changes via K Means Clustering.- Penetrating Machine Learning Servers via Exploiting BMC Vulnerability.