TY  - BOOK
AU  - Kim,Dan Dongseong
AU  - Chen,Chao
ED  - SpringerLink (Online service)
TI  - Machine Learning for Cyber Security: 5th International Conference, ML4CS 2023, Yanuca Island, Fiji, December 4–6, 2023, Proceedings 
T2  - Lecture Notes in Computer Science,
SN  - 9789819724581
AV  - QA76.9.A25 
PY  - 2024///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Data protection
KW  - Computer engineering
KW  - Computer networks 
KW  - Computer networks
KW  - Security measures
KW  - Data and Information Security
KW  - Computer Engineering and Networks
KW  - Mobile and Network Security
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-981-97-2458-1
ER  -