This special session is organised and supported by Al-Nahrain University -College of Engineering -Computer Engineering
- Dr Ahmed H. Y. Al-Noori, Al-Nahrain University , Iraq
- Prof. Dr Bilal Al-Khateeb , University of Anbar ,Iraq
- Dr Shaymaa W. Al-Shammari , Al-Nahrain University , Iraq
- Assist. Professor Dr Duraid Y. Mohammed, Al-Iraqia University, Iraq
- Dr William Baily , University of Sheffield ,UK
- Dr Joshua Meggitt , University of Salford, UK
- Prof. Dr Jamila Harbi , Al-Mustansiriyah University, Iraq
- Assist prof. Dr Hanaa Mohsin Ali Al-Abboodi, University of Babylon, Iraq
Recently Machine learning (ML) and deep learning (DL) have become widely used in different fields. One of
these fields is related to cyber security and digital forensics. ML (and its advanced DL) approaches play a
substantial role in improving cybersecurity and forensics, especially in biometrics authentication, intrusion
detection, digital forensics, and access control. On the other hand, Cyber attackers could breach the
trustworthiness and performance of ML and DL models, such as, injecting malicious data into the training
exploiting the model structure, validating and/or testing sets, and/or modifying hyper-parameters of the
In order to improve performance in cybersecurity and forensics areas, the goal of this special session is to
compile recent research efforts devoted to the study of ML and DL in information security and digital forensicsrelated applications and approaches. For example, improve the quality in spoofing detection, intrusion detection biometrics, authentication, digital forensics, access control, identification, image steganography and steganalysis, deep learning computation and training security, and malicious web content identification, etc..
Specifically, looking for high-quality and unpublished work on recent advances in new ML and DL methodologies that can be applied to a broad range of applications.
The topics of interest include, but are not limited to:
• Object detection and transfer learning.
• ML/DL based forensics and anti-forensics.
• Deep Fake threats and challenges.
• Biometrics authentication and identification.
• ML/DL for video and image processing.
• ML/DL for cryptography protocols.
• Adversarial attacks in deep learning.
• Deep learning for cyber security applications.
• Confidentiality and privacy trust challenges associated with machine and deep learning.
|Prospective authors are invited to submit full-length papers (not exceeding 6 pages) conform to the IEEE format . All papers will be handled and processed electronically via the EDAS online submission system.|