[agents] [CFP] Special Issue on Malware Analysis and Vulnerability Detection Using Machine Learning

[Hammad Afzal]

*Malware Analysis and Vulnerability Detection Using Machine Learning*

Call for Papers

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

·         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

Authors can submit their manuscripts through the Manuscript Tracking System
at

https://mts.hindawi.com/submit/journals/scn/mavd/.



*Submission Deadline:           Friday, 10 April 2020*

*Publication Date                    August 2020*

Papers are published upon acceptance, regardless of the Special Issue
publication date.

*Guest Editors*

Farrukh A. Khan, King Saud University, Riyadh, Saudi Arabia

Muhammad Faisal Amjad: National University of Sciences and Technology,
Islamabad, Pakistan

Yin Zhang: Zhongnan University of Economics and Law, Wuhan,China

Hammad Afzal, National University of Sciences and Technology, Islamabad,
Pakistan
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