Security and Communication Networks

Malware Analysis and Vulnerability Detection Using Machine Learning 2022

Publishing date
01 Mar 2023
Submission deadline
14 Oct 2022

Lead Editor

1King Saud University, Riyadh, Saudi Arabia

2National University of Sciences and Technology, Islamabad, Pakistan

This issue is now closed for submissions.

Malware Analysis and Vulnerability Detection Using Machine Learning 2022

This issue is now closed for submissions.


Continuing to grow in volume and complexity, malware is today one of the major threats faced by the digital world. The intent of malware is to cause damage to a computer or network and often involves performing an illegal or unsanctioned activity that can be used to conduct espionage or receive economic gains. Malware attacks have even started to affect embedded computational platforms such as Internet of Things (IoT) devices, medical equipment, and environmental and industrial control systems. Most modern malware types are complex, and many possess the ability to change code as well as the behavior in order to avoid detection. Instead of relying on traditional defense mechanisms, typically comprising the use of signature-based techniques, there is a need to have a broader spectrum of techniques to deal with the diverse nature of malware.

The variants of malware families share typical behavioral patterns that can be obtained either statically or dynamically. Static analysis typically refers to the techniques that analyze the contents of malicious files without executing them, whereas dynamic analysis considers the behavioral aspects of malicious files while executing tasks such as information flow tracking, function call monitoring, and dynamic binary instrumentation. Machine learning techniques can exploit such static and behavioral artefacts to model the evolving structure of modern malware, therefore enabling the detection of more complex malware attacks that cannot be easily detected by traditional signature-based methods. Non-reliance on signatures makes machine-learning-based methods more effective for newly released (zero-day) malware. Moreover, the feature extraction and representation process can further be improved by using deep learning algorithms that can implicitly perform feature engineering.

This Special Issue aims to attract top-quality original research and review articles covering the latest ideas, techniques, and empirical findings related to malware analysis and machine learning.

Potential topics include but are not limited to the following:

  • Machine learning and/or artificial intelligence in malware analysis
  • Malware analysis for IoT, resource constrained devices, and mobile platforms
  • Malware Intelligence, Hunting and Attribution
  • Software vulnerability prediction with machine learning and/or artificial intelligence
  • Advances in the detection and prevention of zero-day malware attacks, advanced persistent threats, and cyber deception using machine learning and/or artificial intelligence
  • Latest trends in vulnerability exploitation, malware design, and machine learning and/or artificial intelligence
  • Automation of Threat Prevention, Detection and Response
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