Call for papers: Deep Learning Models for Security and Privacy in Industrial Systems

Guest Editors

BB Gupta, National Institute of Technology, Kurukshetra, India (
Dharma P Agrawal, University of Cincinnati, Cincinnati, USA (

Aims and scope

Deep learning automatically identifies features which are more important for classification. It is an enhanced form of machine learning and generally gives an output faster than machine learning algorithms when a sufficiently large amount of data is considered. An industrial system comprises inputs, processes and outputs. Inputs generally consist of raw materials, land, power, transport, cost of infrastructure and other such things considered to be investments. The process is the transformation that the raw materials undergo to be labelled as the finished product. The outputs are the finished product and the profit the producer receives from the finished product. In the current scenario, the basics are the same but the classification is changed slightly. The current scenario also includes the dynamic relation between the demand and supply, along with various factors such as review, alliances between industries and other such dynamic parameters which change randomly.

Implementing deep learning in industrial systems requires an understand of the dynamics of current industrial behaviour, and designing an automation system with respect to the same knowledge. Deep learning networks can identify the dynamics of industry if large enough data sets are provided. This means all the factories will be completely automated, and unlike the current scenario companies will suffer minimum losses on risk factor. These networks might also be able to identify potential mutually beneficial relationships amongst companies which in turn will lead to economic gain for both parties. Deep learning networks will also be able to perform risk analysis, thereby increasing the security of the companies. Another factor which leads to enhanced security and privacy is that there will be no information leakage because no human will be aware as to why a certain decision has been made and when it will change.

This special issue mainly focuses on deep learning models for security and privacy in industrial systems, addressing both original algorithmic development and new applications. We are soliciting original contributions from leading researchers and practitioners in academia as well as industry, which address a wide range of theoretical and application issues in this domain.

Topics of interest

Topics relevant to this special issue include but are not limited to:

  • Threats on security and privacy during transmission of data
  • Automation mechanism implementing deep learning
  • Security mechanisms in industrial systems
  • Sustainable computing design and analysis for IoT environments
  • Nature-inspired smart efficient energy hybrid systems for IoT networks
  • Energy-efficient networking for smart environments
  • Data security and privacy for Cyber physical systems
  • Energy efficiency in the Internet of Things
  • Business models and processes for the Internet of Things
  • Device-to-Device (D2D) communications in industrial systems
  • Learning for anomaly and intrusion detection
  • Autonomous vehicle, and wider transport system, security and privacy
  • Learning for critical infrastructure security
  • Authentication and access control for Cyber physical systems
  • Embedded systems security and privacy
  • Security and privacy in wireless sensor networks
  • Security and privacy in industrial control systems
  • Smart grid security
  • The relation of security and safety for smart industrial systems
  • Secure communications in sensor networks and the IoT
  • Trustworthiness management models for sensor networks and the IoT
  • Cyber security of industrial cyber-physical systems

Papers must be tailored to the emerging fields of deep learning models for security and privacy in industrial systems, through deployments models, challenges and novel solutions. The editors maintain the right to reject papers they deem to be out of scope of this special issue. Only originally, unpublished contributions and invited articles will be considered for the issue. The papers should be formatted according to the journal guidelines. 

Important dates

  • Manuscripts due: April 30, 2020
  • First Decision date: July 30, 2020
  • Revision due: September 15, 2020
  • Final decision date: November 01, 2020
  • Final paper due: December 15, 2020