Background

The aim of this special issue is to concentrate on Deep Learning Applications for Cyber-Physical Systems (CPS) to protect the products and business from malware and hacker attacks. CPSs have become ubiquitous nowadays and are the core of modern critical infrastructure and research applications. These systems are a combination of integrated physical processes, networking and computation to be minored and controlled embedded subsystems through networked systems with feedback loops to change their behavior when necessary. Currently, deep learning is not a silver bullet that can solve all security problems because it needs extensive labeled datasets. Unfortunately, no such labeled datasets are readily available. However, there are several security use cases where deep learning networks make significant improvements to existing solutions. Malware detection and network intrusion detection are two such areas where deep learning has shown significant improvements over the rule-based and classic machine learning-based solutions. At the same time, cybercrime remains a growing challenge in terms of authentication and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis, and forensic identification. Machine learning can be described in many ways. Perhaps the most useful is as type of optimization. This is done via what is known as an objective function, with “objective” used in the sense of a goal. This function, taking data and model parameters as arguments, can be evaluated to return a number resulting in deep learning.

Deep learning methods are heavily resource-intensive, especially when ensembles of multiple models are included. It is expected that the development of deep learning and its related visual analytic methodologies would further affect the field of intelligent surveillance systems. This would address the challenges of using deep learning and related techniques to understand and promote the use of ubiquitous intelligent surveillance systems. Henceforth, deep learning applications for cyber-physical systems with advances in machine learning techniques, improvements in sensors, and ever-greater computing power shall be focused to create a new generation of hardware and software robots with practical applications in nearly every industry sector. This also focuses on the application aspect, which is more related to people’s daily lives, and will present a real-time system including a distributed multi-camera system that integrates computing and communicating capabilities with monitoring on people in the physical world, namely person re-identification in cyber-physical surveillance systems.

To derive optimistic models with security, a special issue, “Deep Learning Applications for Cyber Security”, is proposed. This will facilitate all research and industry groups with optimistic models and quality outcomes.

Topics of Interest

Researchers and practitioners may submit their innovations and findings by applications in deep learning and cyber security. Sample topics are given below:

Submission

We are soliciting original contributions that have not been published and are not currently under consideration elsewhere. Both theoretical studies and state-of-the-art practical applications are welcome. All submitted papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this special section.

We also encourage extensions of conference papers, unless prohibited by copyright, if there is a significant difference in the technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the difference between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors’ own papers and dissertations, should be less than 30%. Each submission must conform to the IJPE template. Please click here to submit your paper.

Special Attention

Important Dates

Guest Editors

About the Guest Editors

Professor Swarnalatha P is an Associate Professor at the School of Computer Science and Engineering, VIT University, Vellore, India. She pursued her Ph.D. in image processing and intelligent systems. She has published more than 160 papers in international journals, international conference proceedings, and national conferences. She has more than nineteen years of teaching experience. She is a member of IACSIT, CSI, ACM, IACSIT, IEEE (WIE), and ACEEE. She is an editorial board member and reviewer of international and national journals and conferences. Her current research interest includes image processing, remote sensing, artificial intelligence, big data analytics, cloud computing, and software engineering.

Professor Prabu S completed a B.S. in Computer Science and Engineering from Sona College of Technology (Autonomous), Master’s in Remote Sensing from College of Engineering Guindy, Anna University Chennai, Master’s in Information Technology at School of Computer Science and Engineering, Bharathidasan University Trichy, and PhD in Integration of GIS and Artificial Neural Networks to Map the Landslide Susceptibility from the College of Engineering Guindy, Anna University, Chennai. He has more than 100 publications in national and international journals and conferences. He has organized three international conferences, which include an IEEE conference as chair, as well as participated in many workshops and seminars. He is a member of many professional bodies and a senior member of IACSIT, UACEE and IEEE. He has more than fifteen years of experience in teaching and research. He is currently a Professor and Head of the School of CSE at Vellore Institute of Technology.

Professor Anantharajah Kaneswaran is Head of the Computer Engineering department and senior lecturer at the University of Jaffna. Dr. Kaneswaran received his PhD from Queensland University of Technology (QUT) in 2015. From July 2008 till July 2011, he worked as an engineer at Sri Lanka Telecom (SLT). He handled the new service provisioning process and fault handling process configuration in Operations Support System (Clarity). He worked as a core team member for the new CRM system implementation team at SLT. He was involved in the CRM system implementation, integrations of the CRM system with OSS and CRM system with BSS. He obtained the most popular thesis (technical research) award from the Smart Services Cooperative Research Centre (Smart Services CRC), Smart Services CRC Postgraduate Scholarship, QUT fee Waiver Scholarship (FEEWAIVE) and QUT Write-up Scholarship while pursuing his PhD degree. He obtained the Mahapola Higher Education (Merit) Scholarship for his performance in Advanced Level Examination. He is a member of IEEE and associate member of the Institution of Engineers Sri Lanka (IESL).

Professor Sureshkumar N is an Associate Professor in School of Computer Science and Engineering , VIT University, Vellore. He did his PhD in image processing and machine learning . He has published more than 100 papers in international journals, international conference proceedings, and national conferences. He has more than 15 years of teaching experience. He is an editorial board member and a reviewer of international and national Journals and conferences. His current research interests include image processing, machine learning, big data analytics, and remote sensing.