Machine Learning Methodologies and Applications in Cybersecurity Data Analysis

Dear Colleagues,

Machine learning (ML) represents a pivotal technology for current and future information systems, with many domains already leveraging its capabilities. However, ML deployment in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. ML is able to quickly analyze large volumes of historical and dynamic data, enabling applications to operationalize data from various sources in near-real time. Recently, we have witnessed the rapid development in ML methodologies and applications for cybersecurity data analysis in threat detection, raw data analysis, and alert management, among others. Yet, in this specific domain, unleashing the full benefits of ML in practice stems from balancing the underlying conflict between the intrinsic characteristics of the cybersecurity domain and the fundamental assumptions of ML. 

This Special Issue aims to collect recent advancements in machine learning methodologies and applications targeted towards tackling cybersecurity data challenges, highly valuing interdisciplinary research to contribute new challenges, research questions, approaches, and datasets related to this topic. 

This Special Issue invites new research contributions to machine learning methodologies and applications specifically tailored to cybersecurity data analysis challenges. The scope includes but is not limited to the following topics:

• ML methods and applications for capturing/handling/evaluating cybersecurity datasets;
• ML methods and applications for data-driven cybersecurity decision making;
• ML methods and applications for security policy rule generation;
• ML methods and applications for protecting valuable security data;
• ML methods and applications for context-aware cybersecurity data analysis;
• ML methods and applications for feature engineering in cybersecurity;
• ML methods and applications for PHY/MAC/L3-L7 security protocol design and evaluation
• ML methods and applications for PHY/MAC/L3-L7 security protocol optimization;
• ML methods and applications for data-driven network protocol fuzzing;
• ML methods and applications for data-driven anomaly/ intrusion detection;
• ML methods and applications for data-driven network traffic analysis;
• ML methods and applications for data-driven endpoint detection and response;
• ML methods and applications for data-driven cybersecurity defense framework;
• Cybersecurity datasets/benchmark for data analysis in ML methods and applications;
• Cybersecurity prototypes/testbeds for data analysis in ML methods and applications, etc.

We look forward to receiving your contributions.  

Prof. Dr. Biao Han
Dr. Xiaoyan Wang
Prof. Dr. Xiucai Ye
Dr. Na Zhao
Guest Editors

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• machine learning
• cybersecurity
• data science
• artificial intelligence