Advances of Machine Learning in Cybersecurity (VSI-mlsec)

Overview:

With the rapid advancement of emerging technologies such as Internet of Things (IoT) and cloud computing, a huge amount of data is generated and processed in our daily life. As these technologies are based on the internet, security issues are continuously increasing due to the presence of numerous hackers and malicious users. They always try to hack users’ personal and confidential data by using security attacks. Sometimes, they replace the authentic data by their fake data. The situation becomes more critical, when a large number of users access and store their personal data outside their own domain at the same time. Attackers mainly target financial, healthcare and defence sectors. Therefore, there must be a strong security technique to protect confidential or personal data against the hackers and malicious users.

Currently, Machine Learning (ML) algorithms are used in the cybersecurity field by many researchers. Machine learning is the study of mathematical model-based algorithms that improve automatically through past experience. ML algorithms are based on data to make decisions without being explicitly programmed to do so. There are many applications of ML in daily life, such as smart email categorization, chatbot, marketing, healthcare, gaming, plagiarism check, autonomous vehicles, and many more. Nowadays, ML is used in industry and academia due to the data-driven feature for achieving enhanced security and privacy. As new attacks are being developed every day by the attackers and malicious users, it is very difficult to detect them by using the traditional intrusion detection techniques. ML algorithms can be developed to train a system for detecting sophisticated attacks, which are similar to the already defined known attacks. It is important to improve the algorithms so that there is a good trade-off between learning cost and detection accuracy. Recent research has also shown the negative impact of ML as these advanced fields support new attack tools by using adversarial ML techniques to develop new attacks. Attackers and malicious users can also hack ML algorithms by altering the training data and modifying the classification function of ML, which can directly affect the detection accuracy of a system. These types of threats are very critical. Therefore, novel techniques of cybersecurity must be developed to protect the system.

This special section gives a platform for researchers, academicians and industry professionals to present their research on ML in the cybersecurity field. It aims to address the challenges and issues of applying ML in cybersecurity. Theoretical as well as experimental research works on the mentioned topics are within the scope of this special section.

Topics:

Suggested topics include:

· Adversarial pedagogy, adversarial models and minimum deterrence level

· Machine learning trends in maintaining security and privacy

· Deep learning trends in maintaining security and privacy

· Security threats, intrusions and malware detection exploiting machine learning methods

· Challenges of black-box attacks in machine learning methods

· ML driven attack model generation and specification

· ML based cryptanalysis of cryptographic protocols

· Use of machine learning in forensics and threat intelligence

· ML driven software testing and threat anticipation

· ML driven security architectures

• ML based secure social media
  • ML for multimedia data security
• ML for big data security/cloud security/IoT security
• Emerging technologies and future work directions in cybersecurity

Submission Guidelines:

New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.

Before submission, authors should carefully read the Guide for Authors available at

https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors

Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-mlsec” under the “Issues” tab.

For additional questions, contact the Main Guest Editor.

Schedule:

· Submission of manuscript: May 31, 2021

· First notification: July 31, 2021

· Submission of revised manuscript: August 31, 2021

· Notification of the re-review: September 30, 2021

· Final notification: October 31, 2021

· Final paper due: November 30, 2021

· Publication: March 2022

Guest Editors:

Dr. Suyel Namasudra (Managing Guest Editor)

National Institute of Technology Patna, Bihar, India

Email: suyel.cs@nitp.ac.in, suyelnamasudra@gmail.com

Prof. Ruben Gonzalez Crespo

International University of La Rioja (UNIR), Spain

Email: ruben.gonzalez@unir.net

Dr. Sathish Kumar

Cleveland State University, USA

Email: s.kumar13@csuohio.edu

Biographies of the Guest Editors:

Dr. Suyel Namasudra is an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Patna, Bihar, India. Prior to joining the National Institute of Technology Patna, he was an Assistant Professor in the Department of Computer Science Engineering at the Bennett University, India. He has received PhD in Computer Science and Engineering from National Institute of Technology Silchar, Assam, India. His research interests include Computer Networks, Cloud Computing, Information Security and DNA Computing. Dr. Namasudra has edited 1 book and 30 publications in refereed journals, book chapters and conference proceedings. He has participated in many international conferences as an Organizer and Session Chair. He has also served as a Lead Guest Editor of Computer, Materials & Continua (IF: 4.89). Dr. Namasudra is a member of the Editorial Board and Reviewer of many journals.

Prof. Ruben Gonzalez Crespo has received PhD in Computer Science Engineering. Currently, he is the Vice-Rector of Academic and Professorate Affairs of UNIR, Spain. He is the Editor-in-Chief of the International Journal of Interactive Multimedia and Artificial Intelligence, and an editorial board member of many indexed journals. His main research areas are Soft Computing, Accessibility and TEL. He is an advisory board member of the Ministry of Education, Colombia and evaluator of National Agency for Quality Evaluation and Accreditation of Spain (ANECA).

Dr. Sathish Kumar received his Ph.D. degree in Computer Science and Engineering from University of Louisville, Louisville, KY, USA. He is currently an Associate Professor of Computer Science at Cleveland State University, Cleveland, OH, USA and IEEE Senior Member. He was also a recipient of Summer Faculty Fellowship award from Air Force Research Laboratory in 2020. Up to now, he has authored/co-authored more than 60 scientific articles in established journals including Elsevier, Springer, IEEE Transactions and IEEE Access and reputed conferences. Also, he is Associate Editor of IEEE Access journal. He is a reviewer of several IEEE transactions journals. His research interests focus on techniques for achieving security and privacy, Internet of Things, Blockchain, data science and analytics, and machine learning /artificial intelligence systems. Dr. Kumar’s research has been supported by the National Science Foundation (NSF), Air Force Research Laboratory (AFRL), and National Institutes of Health. Dr. Kumar is a Senior member of IEEE and member of ACM.