Special Issue "Adversarial Intelligence: Secrecy, Privacy, and Robustness"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 18 February 2022.

Special Issue Editors

Dr. Zuxing Li
E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Tongji University, Shanghai, China
Interests: physical-layer security; adversarial reinforcement learning; data privacy; game theory; statistical inference
Dr. Milos Radovanovic
E-Mail Website
Guest Editor
Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia
Interests: machine learning; data mining; adversarial attacks; IoT security; complex network analysis
Prof. Dr. Syed A. Jafar
grade E-Mail Website
Guest Editor
Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA 92697-2625, USA
Interests: network information theory; wireless communication; network coding

Special Issue Information

Dear Colleagues,

Intelligent systems have been widely deployed and have significantly improved the efficiency of communication, transportation, robots, energy systems, etc. Powerful intelligence relies on a great amount of high-quality data, which however can be attacked or maliciously exploited and results in variant adversarial problems.

Research on adversarial intelligence has attracted more attention recently. In addition to improving intelligence, the design of adversarial intelligent systems also considers security, privacy, and robustness issues against active or passive attacks. Theoretic studies on the modeling, assessment, and fundamental bound of adversarial intelligent systems can be made from information-theoretic security, physical-layer security, differential privacy, or game theory. From a practice aspect, adversarial learning algorithms can be developed to improve security, privacy, or robustness. Furthermore, the adversarial problems of emerging applications, e.g., blockchain, need to be addressed.

This Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas:

  • Information theoretic security
  • Communication and physical-layer security
  • Data privacy and anonymity
  • Game theoretic modeling of attacks
  • Adversarial learning
  • Security of cyberphysical systems
  • Blockchain security and privacy
  • Secure emerging applications

Dr. Zuxing Li
Dr. Milos Radovanovic
Prof. Dr. Syed A. Jafar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • secure intelligent system
  • assessment of secure intelligent systems
  • secure scheme of intelligent systems
  • physical-layer security
  • data privacy
  • adversarial learning
  • secure emerging applications

Published Papers (2 papers)

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Research

Article
Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness
Entropy 2021, 23(12), 1650; https://0-doi-org.brum.beds.ac.uk/10.3390/e23121650 - 08 Dec 2021
Viewed by 429
Abstract
In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide [...] Read more.
In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT- SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum–Blum–Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme. Full article
(This article belongs to the Special Issue Adversarial Intelligence: Secrecy, Privacy, and Robustness)
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Article
(Semi-)Automatically Parsing Private Protocols for In-Vehicle ECU Communications
Entropy 2021, 23(11), 1495; https://0-doi-org.brum.beds.ac.uk/10.3390/e23111495 - 11 Nov 2021
Viewed by 374
Abstract
In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, [...] Read more.
In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle’s resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network. Full article
(This article belongs to the Special Issue Adversarial Intelligence: Secrecy, Privacy, and Robustness)
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