Wireless Communications and Mobile Computing

Computational Intelligence Techniques for Information Security and Forensics in IoT Environments


Publishing date
01 May 2021
Status
Closed
Submission deadline
18 Dec 2020

Lead Editor

1Nanjing University of Information Science & Technology, Nanjing, China

2Lakehead University, Thunder Bay, Canada

3National Dong Hwa University, Hualien, Taiwan

4Sejong University, Seoul, Republic of Korea

5University of Milano, Crema, Italy

6Indian Institute of Technology, Jodhpur, India

This issue is now closed for submissions.
More articles will be published in the near future.

Computational Intelligence Techniques for Information Security and Forensics in IoT Environments

This issue is now closed for submissions.
More articles will be published in the near future.

Description

As a popular paradigm, the Internet-of-Things (IoT) usually consists of numerous smart devices interconnected with other devices, application services, and systems by wireless networks and protocols. These smart devices often collect a massive amount of data from real-world objects via sensors and process, transmit and manage these data to networks for a variety of practical applications such as smart cities, smart homes, autonomous transportation, and health supervision. This raises two problems that must be addressed urgently; how to ensure the security of these IoT data, and how to implement the forensics of these data if they suffer illegal copying, tampering, and forgery attacks in IoT environments.

Computational intelligence techniques have shown the desirable efficiency and effectiveness in data processing and mining, and they have attracted a lot of attention. Recently, a typical example is deep convolutional neural networks (CNN) and its derivates, such as Faster Region-based CNN (Faster R-CNN) and Generative Adversarial Networks (GAN). These examples have gained great success in many basic computer vision tasks including automatic image representation generation, semantic segmentation and classification, object detection and tracking, and data restoration. They have outperformed traditional methods significantly. Due to the promising performances of computational intelligence techniques in data processing and mining tasks of computer vision, it might be reasonable and effective to explore these computational intelligence techniques to address the problems of information security and forensics in IoT environments.

The aim of this Special Issue is to solicit original research articles and review articles that focus on the challenges and issues of information security and forensics in IoT environments by computational intelligence techniques and models.

Potential topics include but are not limited to the following:

  • Computational intelligence theories and methodologies for IoT data security
  • Deep learning feature-based IoT data search
  • Perceptual visual content representation, modelling, and understanding in IoT
  • Privacy data control and management in IoT
  • Steganography and steganalysis by Generative Adversarial Networks (GAN) or other deep learning networks in IoT
  • Secret image sharing/visual cryptography by computational intelligence techniques in IoT
  • Digital watermarking schemes by deep learning techniques in IoT
  • Digital forensics by deep learning techniques in IoT
  • Forensics of deep fake faces generated by GAN
  • Fingerprint liveness forensics in IoT
  • IoT copy data/tamper data detection by computational intelligence techniques
  • Identity authentication in IoT
  • Blockchain techniques for tamper-proofing of IoT data or other security applications

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6629591
  • - Research Article

The Named Entity Recognition of Chinese Cybersecurity Using an Active Learning Strategy

Bo Xie | Guowei Shen | ... | Yunhe Cui
  • Special Issue
  • - Volume 2021
  • - Article ID 6617959
  • - Research Article

Privacy-Guarding Optimal Route Finding with Support for Semantic Search on Encrypted Graph in Cloud Computing Scenario

Bin Wu | Xianyi Chen | ... | Caicai Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 6669081
  • - Research Article

Label Rectification Learning through Kernel Extreme Learning Machine

Qiang Cai | Fenghai Li | ... | Shanshan Li
  • Special Issue
  • - Volume 2021
  • - Article ID 6666211
  • - Research Article

A Routing Algorithm for the Sparse Opportunistic Networks Based on Node Intimacy

Gang Xu | Xinyue Wang | ... | Liqiang He
  • Special Issue
  • - Volume 2021
  • - Article ID 6675841
  • - Research Article

On the Optimization Strategy of EV Charging Station Localization and Charging Piles Density

Wenzao Li | Lingling Yang | ... | Xi Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 6660869
  • - Research Article

Motion-Compensated Frame Interpolation Using Cellular Automata-Based Motion Vector Smoothing

Ran Li | Ying Yin | ... | Lei You
  • Special Issue
  • - Volume 2021
  • - Article ID 6638936
  • - Research Article

Time Slot Detection-Based -ary Tree Anticollision Identification Protocol for RFID Tags in the Internet of Things

Xiaojiao Yang | Bizao Wu | ... | W. G. Will Zhao
  • Special Issue
  • - Volume 2021
  • - Article ID 6640106
  • - Research Article

Antiforensics of Speech Resampling Using Dual-Path Strategy

Diqun Yan | Yongkang Gong | Tianyun Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 6631585
  • - Research Article

Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction

Yanfen Le | Shijialuo Jin | ... | Heng Yao
  • Special Issue
  • - Volume 2021
  • - Article ID 8859088
  • - Research Article

Deep Camera-Aware Metric Learning for Person Reidentification

Wei Liu | Ping Liang | ... | Xin Xu
Wireless Communications and Mobile Computing
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Acceptance rate33%
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CiteScore2.900
Impact Factor1.819
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