Special Issue "Security and Computing in Internet of Things"

Submission Deadline: 31 December 2020 (closed)
Submit to Special Issue
Guest Editors
Mr. Partha Pratim Ray, Sikkim University, India
Dr. Nour Moustafa, Theme Lead of Intelligent Security and Lecturer in Cyber Security, The University of New South Wales, Canberra @ADFA, Australia
Dr. Kien Nguyen, Graduate School of Engineering, Chiba University, Japan


Heterogeneity and non-standardization of Internet of Things (IoT) systems cause security and privacy issues. Recent growth of smart technologies has leveraged various new ways of destructing security and privacy of IoT ecosystems. Although, scientific works have proven their effectiveness in catering such issues in IoT, they don’t conform completely with the mitigation of notions of IoT like heterogeneity, non-standardization, flexibility, embedded intelligence, and scalability. New programming technique, tools, platforms as well as novel IoT-centric design and developments are very much needed to mitigate privacy and security aspects in IoT.


We encourage or especially welcome authors to contribute their unpublished work in the related fields of research. Articles containing novel applications, concept, and demonstrations could be submitted in this special issue. State-of-the-art about recent advancements on these areas could be explored. Furthermore, survey, tutorial and systematic reviews in this regard are also welcome.


Potential topics include but are not limited to the following:

• Big data analytics for heterogeneous IoT systems

• Security models based AI for protecting IoT networks

• Federated models for privacy and Security of IoT services

• Privacy-preserving and Blockchain for IoT networks

• Intrusion Detection for IoT systems

• Threat models for IoT systems

• Light-weight programming platform for development of IoT security and privacy

• Embedded systems security for IoT ecosystem

• Machine and deep learning for solving security in IoT

• Novel applications in IoT-based security and privacy

• Use case scenarios in IoT security

• Novel architectures, concepts, and models in IoT security

• Survey, review, tutorial in IoT security and privacy


Warm reminder: Please select Special Issue: Security and Computing in Internet of Things when you submit your article in CMC submission system

IoT, Security, Computing, Pervasive Computing

Published Papers
  • Non-Associative Algebra Redesigning Block Cipher with Color Image Encryption
  • Abstract The substitution box (S-box) is a fundamentally important component of symmetric key cryptosystem. An S-box is a primary source of non-linearity in modern block ciphers, and it resists the linear attack. Various approaches have been adopted to construct S-boxes. S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields, unitary commutative rings and cyclic and non-cyclic finite groups. In this paper, first a non-associative ring of order 512 is obtained by using computational techniques, and then by this ring a triplet of 8 × 8 S-boxes is designed. The motivation behind the designing of these S-boxes is… More
  •   Views:199       Downloads:117        Download PDF

  • A Secure NDN Framework for Internet of Things Enabled Healthcare
  • Abstract Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption… More
  •   Views:102       Downloads:51        Download PDF

  • Approach for Training Quantum Neural Network to Predict Severity of COVID-19 in Patients
  • Abstract Currently, COVID-19 is spreading all over the world and profoundly impacting people’s lives and economic activities. In this paper, a novel approach called the COVID-19 Quantum Neural Network (CQNN) for predicting the severity of COVID-19 in patients is proposed. It consists of two phases: In the first, the most distinct subset of features in a dataset is identified using a Quick Reduct Feature Selection (QRFS) method to improve its classification performance; and, in the second, machine learning is used to train the quantum neural network to classify the risk. It is found that patients’ serial blood counts (their numbers of… More
  •   Views:398       Downloads:226        Download PDF