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MAAIDL 2020 : Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'


When N/A
Where N/A
Submission Deadline Jul 31, 2020
Notification Due Aug 7, 2020
Final Version Due Aug 31, 2020
Categories    malware analysis   machine learning   artificial intelligence   deep learning

Call For Papers

You are welcome to submit your contributions to the Springer Book on Malware Analysis using Artificial Intelligence and Deep Learning.

More information is given on the webpage:

Malicious software is one of the most serious threats to information security today. Malware analysis is a fast-growing field demanding a great deal of attention because of remarkable progress in social networks, cloud and web technologies, e-commerce, mobile environments, smart grids, Internet of Things (IoT), etc. Due to this evolving cyber threat landscape, legacy solutions built on specified rule sets, such as signature-driven security capabilities, cannot scale to fully meet the demand of advanced malware and other cybercrime detection and prevention.

Artificial Intelligence (AI) and Deep Learning (DL) techniques have been successfully applied to many computer applications. These solutions often provide significant improvements as compared to more traditional machine learning methods and have resulted in new industry standards in highly cognitive tasks, ranging from natural language processing to self-driving cars. However, a relatively limited number of studies have applied these powerful techniques to malware analysis.

This book is designed to fill the gap between DL/AI and malware research. Proposed topics should include modern and practical DL and AI techniques. This book will be particularly timely.

The submission webpage:

*** EDITORS ***
∙ Mark Stamp (
∙ Mamoun Alazab (
∙ Andrii Shalaginov (

*** TIMELINE ***
∙ July 31, 2020 — Chapters due to editors
∙ August 7, 2020 — Notification to authors
∙ August 31, 2020 — Camera ready chapters (typeset in LATEX) due to editors

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