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Electronics
Special Issues
Data-Driven Network Security and Privacy
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Data-Driven Network Security and Privacy

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 7739

Special Issue Editors

Dr. Donghyun Kim

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Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: security, privacy, and advanced computing technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Mingon Kang

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Guest Editor
Department of Computer Science; University of Nevada, Las Vegas, NV 89154, USA
Interests: bioinformatics; machine learning; data mining; computer vision; and big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Daehee Seo

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Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: cybersecurity
Dr. Junggab Son

E-Mail Website
Guest Editor
Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Interests: applied cryptography in cybersecurity; blockchain and smart contract; data science in cybersecurity

Special Issue Information

Dear Colleagues,

The recent advances in machine learning and artificial intelligent technologies have enabled the analysis of big data, which resulted in various disruptive innovations. Such development has increased the significance of data and data analytics in the area of network security and privacy. That is, while malicious adversaries have been empowered by the emerging technologies and become capable of launching new attacks against network security and privacy, a number of innovative data-driven approaches have been developed to detect and/or hinder cyber threats, either.

This special issue solicits high-quality contributions with consolidated and thoroughly evaluated research related to data-driven approaches for network security and privacy that are worthy of archival publication in the journal. It is intended to provide a summary of research that identifies new network attack strategies using data-driven approaches as well as innovative data-driven methods to tackle network security and privacy problems. This special issue will serve as a comprehensive collection of the current state-of-the-art technologies within the context.

Topics of interest include but are not limited to the following:
  • Data-driven cyber threat
  • Data-driven abnomality detection
  • Data-driven incident analysis
  • Data-driven network vulnerability
  • Data-driven privacy infringement
  • Data-driven threat anticipation
  • Data analytics and visualization
  • Machine learning and/or Deep learning-based security solutions
  • Machine learning and/or Deep learning-based privacy protection
  • AI for Network security and privacy

Dr. Donghyun Kim
Dr. Mingon Kang
Dr. Daehee Seo
Dr. Junggab Son
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 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.

Published Papers (2 papers)

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Research

20 pages, 1040 KiB  
Article
Distributed Blockchain-Based Message Authentication Scheme for Connected Vehicles
by Jaewon Noh, Sangil Jeon and Sunghyun Cho
Electronics 2020, 9(1), 74; https://doi.org/10.3390/electronics9010074 - 1 Jan 2020
Cited by 33 | Viewed by 4602
Abstract
Vehicular ad-hoc networks (VANETs) have several security issues such as privacy preservation, secure authentication, and system reliability. In the VANET, a vehicle communicates with other vehicles or infrastructures using broadcasting messages. These messages contain not only normal traffic information, but also identification information [...] Read more.
Vehicular ad-hoc networks (VANETs) have several security issues such as privacy preservation, secure authentication, and system reliability. In the VANET, a vehicle communicates with other vehicles or infrastructures using broadcasting messages. These messages contain not only normal traffic information, but also identification information of sender. In general, the identification information remains encrypted to ensure privacy. However, the conventional centralized system can decrypt the identification information using private information of the sender vehicle. As a result, the central server can often be targeted by adversaries. We propose a message authentication scheme for anonymity and decentralization of information using blockchain technology. Here, we introduce public-private key and message authentication code (MAC) for secure authentication. In this paper, we adopt consensus algorithms for composing blockchain system such as the proof of work (PoW) and Practical Byzantine Fault Tolerance (PBFT) into the proposed authentication process. Finally, we demonstrate that the proposed method is secure from the attacks which include impersonation from internal attacker as well as typical attacks. Full article
(This article belongs to the Special Issue Data-Driven Network Security and Privacy)
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13 pages, 979 KiB  
Article
Tree Sampling for Detection of Information Source in Densely Connected Networks
by Taewon Min and Changhee Joo
Electronics 2019, 8(5), 587; https://doi.org/10.3390/electronics8050587 - 27 May 2019
Cited by 1 | Viewed by 2519
Abstract
We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these [...] Read more.
We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O ( n 2 log n ) , where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks. Full article
(This article belongs to the Special Issue Data-Driven Network Security and Privacy)
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