Special lssues
Table of Content

Security, Privacy, Trust, and Computational Intelligence in IoT Systems

Submission Deadline: 31 January 2022 (closed)

Guest Editors

Dr. Muhammad Shafiq, Yeungnam University, South Korea.
Dr. Azeem Irshad, International Islamic University, Pakistan.
Dr. Jin-Ghoo Choi, Yeungnam University, South Korea.


The key driving factors behind the networking advancements are the Internet and its modern paradigms such as the Internet of Things (IoT), Industrial IoT (IIoT), Internet of Medical Things (IoMT), and others. There will be more than 75 billion devices connected to the Internet by 2025. Various studies anticipate that the recently coined 5G technology could not even meet the demand of the Internet by the extensively growing IoT devices after 2030. The deployment of connected devices at such a large-scale will hugely increase the scope of security, privacy, and trust concerns along with the communication and computational costs while managing the big data of their inbuilt sensors. To this end, the lower computational cost, faster speed, and extended security, privacy, and trust prerequisites are the most unprecedented issues of the systems connected to cyberspace at a large-scale. Further, the increased number of sophisticated cyber-attacks and privacy loopholes over the Internet-connected systems and technology makes cybersecurity and privacy a challenging discipline. Therefore, the advanced techniques of Computational Intelligence (CI) are of much interest to make connected systems smart, secure, and reliable. The CI-based techniques like Artificial Neural Networks (ANNs) and fuzzy logic are good candidate solutions to security, privacy, and trust due to their salient features such as high accurac y and low computational cost. However, the limitations of the CI-techniques such as over-fitting in ANNs and the rule tuning in fuzzy logic are the pertinent issues that equally require hybrid techniques of CI (e.g., neuro-fuzzy systems, fuzzy inductive reasoning, fuzzy and heterogeneous neural networks, genetic-fuzzy systems, etc.) to solve complex security problems. The swarm intelligence-based techniques are also expanding the scope of CI due to the growing concerns of privacy leakage and security attacks in connected systems. Moreover, the distributed ledger-based databases of blockchain technology and the theoretical and algorithmic advancements in evolutionary computation, genetic algorithms, learning theory, self-organizing systems, cognitive computing, cellular automata, and probabilistic methods are significant while designing cognitive solutions for connected systems in cyberspace against evolving security and privacy attacks.

The up-to-date multi-aspect developments in CI are interesting to equip Internet-connected systems with a proactive resilience and an intelligent reactive defense against cyber-attacks. The aim of this special issue is to provide a state-of-the-art reference regarding theoretical and algorithmic advancements of CI methods for ensuring “security, privacy, and trust” of the systems deployed in the cyber domain including IoT, IIOT, IoMT, etc. on a large scale.


The topics of interest for this special issue include, but are not limited to:

• CI-based security-hardened and privacy-preserved connected systems.

• Evolutionary computing and swarm intelligence-based trust control in connected systems.

• Nature-inspired CI-based security, privacy, and trust in large-sc ale connected systems

• Advanced deep learning cyberspace intrusion detection algorithms for connected systems.

• CI-based biometric modalities involved in security, privacy, and trust control for connected systems in cyberspace.

• Security, privacy, and trust solutions in reconfigurable intelligent surfaces of 6G and beyond UAV-assisted wireless networks.

• CI-based privacy-preserved, anonymization, and pseudonymization methods for Internet-connected systems.

• Security, privacy, and trust assurance protocols in self-sustainable wireless networks.

• CI-based trust and confidence analysis of Internet-connected systems.

• Optimizing security, privacy, and trust in intelligent connected systems using evolutionary techniques.

• CI-based design and analysis of cyberspace vulnerabilities detection and threat prevention systems.

• Fuzzy neural networks and probabilistic methods for intrusion detection analysis in large-scale IoT systems.

• Advanced cybersecurity solutions involving hybrid techniques of CI including fuzzy inductive reasoning, evolutionary algorithms, recurrent neural nets, extreme learning machines, genetic algorithms, deep neural nets, etc.

• CI-based resilience models for advanced security and privacy threats in cyberspace.

• Artificial intelligence- based security, privacy, and trust for Internet-connected systems.

• Innovative CI-based techniques for prevention, detection, and mitigation of cybersecurity attacks.

• Advanced computational models for intelligent and trusted IoT, IIoT, and IoMT.

• Applications of innovative CI techniques for connected systems in different areas including IoT, IIoT, IoT, and others.


Computational Intelligence;

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