Special Issue "Intelligent Models for Security and Resilience in Cyber Physical Systems"

Submission Deadline: 31 July 2020 (closed)
Guest Editors
Professor Qi Liu, Nanjing University of Information Science and Technology, China
Professor Xiaodong Liu, Edinburgh Napier University, UK
Professor Radu Grosu, Vienna University of Technology, Austria
Professor Nigel Linge, University of Salford, UK
Professor Ching-Nung Yang, National Dong Hwa University, Taiwan

Summary

With the extensive growth of novel hardware and software compositions creating smart, autonomously acting devices in recent years, Cyber Physical Systems (CPS) become attractive enabling efficient end-to-end workflows and new forms of user-machine interaction. On one hand, these CPS applications can provide critical services in various emerging application domains such as energy management, health care, traffic control, industrial measurement and surveillance, etc. performance of internal functionality and processes highly relies on the design and optimization of intelligent models, security algorithms and resilient components due to their heterogeneous, evolving and distributed nature.


Recent advancement of Artificial Intelligence and Cyber Security has been well investigated and applied to CPS systems, but still meets the challenges in a large-scale distributed CPS system. In addition, resilient services are critically required in a CPS to provide "acceptance-level" operational normalcy, e.g. state awareness, disturbance recognition and responses, etc.; nonetheless, existing approaches and tools are only able to support limited resilience in a non-dynamic manner, i.e., fail to consider and respond to a comprehensive profile of run-time situation without needs of the devices and individuals in a CPS. In terms of a large-scale CPS, more issues need to be addressed such as how to securely maintain the resilient services, how to introduce intelligent responses in a CPS based on retrieved and/or recognized run-time states, and so on.


This special issue is intended for researchers, engineers and practitioners from both academia and industry, who are interested in issues on intelligent models, cyber security and resilience in large-scale CPS systems.


Potential topics include but are not limited to: 
1. Intelligent Models in a Cyber Physical System
2. Data Security in a Cyber Physical System
3. Service Resilience in a Cyber Physical System
4. Machine Learning for Cyber Security and Privacy Protection in a CPS
5. AI and Deep Learning in an Industrial CPS
6. CPS Data Hiding in Plain- or Cipher-Formats
7. Threat Detection and Reliable Computing in a CPS
8. Industrial CPS and its Transmission Security
9. Perception, Recognition and Resilient Responses in a Large-Scale CPS
10. Process Resilience and Adaptation in a Large-Scale CPS


Keywords
Intelligent Models; Data Security; Service Resilience; Cyber Physical Systems; Machine Learning and Deep Learning; Threat Detection and Reliability; Process Resilience and Adaptation

Published Papers
  • Run-Time Dynamic Resource Adjustment for Mitigating Skew in MapReduce
  • Abstract MapReduce is a widely used programming model for large-scale data processing. However, it still suffers from the skew problem, which refers to the case in which load is imbalanced among tasks. This problem can cause a small number of tasks to consume much more time than other tasks, thereby prolonging the total job completion time. Existing solutions to this problem commonly predict the loads of tasks and then rebalance the load among them. However, solutions of this kind often incur high performance overhead due to the load prediction and rebalancing. Moreover, existing solutions target the partitioning skew for reduce tasks,… More
  •   Views:17       Downloads:12        Download PDF

  • An Anonymous Authentication Scheme with Controllable Linkability for Vehicle Sensor Networks
  • Abstract Vehicle sensor networks (VSN) play an increasingly important part in smart city, due to the interconnectivity of the infrastructure. However similar to other wireless communications, vehicle sensor networks are susceptible to a broad range of attacks. In addition to ensuring security for both data-at-rest and data-in-transit, it is essential to preserve the privacy of data and users in vehicle sensor networks. Many existing authentication schemes for vehicle sensor networks are generally not designed to also preserve the privacy between the user and service provider (e.g., mining user data to provide personalized services without infringing on user privacy). Controllable linkability can… More
  •   Views:257       Downloads:165        Download PDF

  • Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things
  • Abstract Internet of Things (IoT) is a network that connects things in a special union. It embeds a physical entity through an intelligent perception system to obtain information about the component at any time. It connects various objects. IoT has the ability of information transmission, information perception,andinformationprocessing.Theairqualityforecastinghasalways been an urgent problem, which affects people’s quality of life seriously. So far, many air quality prediction algorithms have been proposed, which can be mainly classifed into two categories. One is regression-based prediction, the other is deep learning-based prediction. Regression-based prediction is aimed to make use of the classical regression algorithm and the various… More
  •   Views:810       Downloads:446        Download PDF

  • Performance Analysis of Intelligent CR-NOMA Model for Industrial IoT Communications
  • Abstract Aiming for ultra-reliable low-latency wireless communications required in industrial internet of things (IIoT) applications, this paper studies a simple cognitive radio non-orthogonal multiple access (CR-NOMA) downlink system. This system consists of two secondary users (SUs) dynamically interfered by the primary user (PU), and its performance is characterized by the outage probability of the SU communications. This outage probability is calculated under two conditions where, a) the transmission of PU starts after the channel state information (CSI) is acquired, so the base station (BS) is oblivious of the interference, and b) when the BS is aware of the PU interference, and… More
  •   Views:569       Downloads:318        Download PDF