Springer Book 'Malware Analysis using Artificial Intelligence and Deep Learning'
MAAIDL 2020
- Event Date: ~
- Submission Date: 2020-07-31
You are welcome to submit your contributions to the Springer Book on Malware Analysis using Artificial Intelligence and Deep Learning.
*** INTRODUCTION ***
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.
*** SUBMISSION GUIDELINES ***
TBA
*** EDITORS ***
∙ Mark Stamp (mark.stamp@sjsu.edu)
∙ Mamoun Alazab (mamoun.alazab@cdu.edu.au)
∙ Andrii Shalaginov (andrii.shalaginov@ntnu.no)
*** 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
*** INTRODUCTION ***
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.
*** SUBMISSION GUIDELINES ***
TBA
*** EDITORS ***
∙ Mark Stamp (mark.stamp@sjsu.edu)
∙ Mamoun Alazab (mamoun.alazab@cdu.edu.au)
∙ Andrii Shalaginov (andrii.shalaginov@ntnu.no)
*** 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
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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