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Electronics
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Novel Approaches in Cybersecurity and Privacy Protection
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Novel Approaches in Cybersecurity and Privacy Protection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (9 September 2023) | Viewed by 18357

Special Issue Editor

Dr. Qing Tan

E-Mail Website
Guest Editor
School of Computing and Information Systems Athabasca University, Athabasca, Canada
Interests: location based technologies; mobile computing and technologies; wireless sensors networks; network technologies and cyber security; cloud computing; enterprise modeling; and robotics and robot telepresence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this big data and AI era, digital data have exponentially increased in recent years, with great variety. While various data carrying personal information have been purposely entered online, a large amount of data have been logged from people’s online footprints. Some data that appear to be irrelevant can be processed with AI-based analytic tools to infer personal and sensitive information. Privacy-enhancing technologies (PET) have been developed to maximize data usability and minimize personal privacy violations. The recently launched ChatGPT demonstrated its powerful capability as collective intelligence that can have a significant impact on information security and privacy protection. This Special Issue calls for researchers to publish their original studies on how AI and big data analytics impact information security.

  • AI ChatBots on Information Security and Data Privacy.
  • Application and Evaluation of Privacy-Enhancing Technology.
  • Behavior Study on Information Technology.

Dr. Qing Tan
Guest Editor

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 submissions that pass pre-check are 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • ChatGPT and impact on privacy and security
  • human agency in IT
  • big data privacy
  • privacy-enhancing technology
  • behavior in IT

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Published Papers (3 papers)

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Research

15 pages, 683 KiB  
Article
DoubleStrokeNet: Bigram-Level Keystroke Authentication
by Teodor Neacsu, Teodor Poncu, Stefan Ruseti and Mihai Dascalu
Electronics 2023, 12(20), 4309; https://doi.org/10.3390/electronics12204309 - 18 Oct 2023
Cited by 5 | Viewed by 1739
Abstract
Keystroke authentication is a well-established biometric technique that has gained significant attention due to its non-intrusive and continuous characteristics. The method analyzes the unique typing patterns of individuals to verify their identity while interacting with the keyboard, both virtual and hardware. Current deep-learning [...] Read more.
Keystroke authentication is a well-established biometric technique that has gained significant attention due to its non-intrusive and continuous characteristics. The method analyzes the unique typing patterns of individuals to verify their identity while interacting with the keyboard, both virtual and hardware. Current deep-learning approaches like TypeNet and TypeFormer focus on generating biometric signatures as embeddings for the entire typing sequence. The authentication process is defined using the Euclidean distances between the new typing embedding and the saved biometric signatures. This paper introduces a novel approach called DoubleStrokeNet for authenticating users through keystroke analysis using bigram embeddings. Unlike conventional methods, our model targets the temporal features of bigrams to generate user embeddings. This is achieved using a Transformer-based neural network that distinguishes between different bigrams. Furthermore, we employ self-supervised learning techniques to compute embeddings for both bigrams and users. By harnessing the power of the Transformer’s attention mechanism, the DoubleStrokeNet approach represents a significant departure from existing methods. It allows for a more precise and accurate assessment of user authenticity, specifically emphasizing the temporal characteristics and latent representations of bigrams in deriving user embeddings. Our experiments were conducted using the Aalto University keystrokes datasets, which include 136 million keystrokes from 168,000 subjects using physical keyboards and 63 million keystrokes acquired on mobile devices from 60,000 subjects. The DoubleStrokeNet outperforms the TypeNet-based authentication system using 10 enrollment typing sequences, achieving Equal Error Rate (EER) values of 0.75% and 2.35% for physical and touchscreen keyboards, respectively. Full article
(This article belongs to the Special Issue Novel Approaches in Cybersecurity and Privacy Protection)
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19 pages, 475 KiB  
Article
An Efficient Attribute-Based Encryption Scheme with Data Security Classification in the Multi-Cloud Environment
by Guangcan Yang, Peixuan Li, Ke Xiao, Yunhua He, Gang Xu, Chao Wang and Xiubo Chen
Electronics 2023, 12(20), 4237; https://doi.org/10.3390/electronics12204237 - 13 Oct 2023
Cited by 6 | Viewed by 2260
Abstract
As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized [...] Read more.
As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized as the best access control method for safeguarding the cloud storage environment, and numerous solutions based on ABE have been developed successively. However, the majority of current research is conducted within a single cloud provider, and only the limited number of schemes for the multi-cloud environment also fail to support the data security classification on the cloud side. Therefore, we propose an efficient attribute-based encryption scheme with data security classification in the multi-cloud environment. In our scheme, the data owner’s data are divided into two security levels and stored in different cloud providers, which improves the security of outsourcing data. Moreover, based on Ciphertext-Policy Attribute-Based Encryption (CP-ABE), our scheme can not only provide a fine-grained access control for the data user, but also completely exploit the cloud side to facilitate outsourcing decryption to lighten the data user’s computing load. The security analysis showed that our scheme is effective against selective-attribute plaintext attack, as well as protects the privacy of the data. The experimental results also demonstrated that the computational overhead is obviously less than other existing schemes. Full article
(This article belongs to the Special Issue Novel Approaches in Cybersecurity and Privacy Protection)
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34 pages, 603 KiB  
Article
A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems
by Nicholas Jeffrey, Qing Tan and José R. Villar
Electronics 2023, 12(15), 3283; https://doi.org/10.3390/electronics12153283 - 30 Jul 2023
Cited by 58 | Viewed by 13559
Abstract
Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory [...] Read more.
Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges. Full article
(This article belongs to the Special Issue Novel Approaches in Cybersecurity and Privacy Protection)
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