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Electronics
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New Trends in Information Security
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New Trends in Information Security

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 6243

Special Issue Editors

Dr. Jheng-Jia Huang

E-Mail Website
Guest Editor
Department of Information Management, National Taiwan University of Science and Technology, 43 Keelung Rd., Sect. 4, Taipei 106, Taiwan
Interests: information and communication security; applied cryptology; secure protocol design; security management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ray-Lin Tso

E-Mail Website
Guest Editor
Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
Interests: cryptography; information security; post-quantum; blockchain and privacy enhancement
Dr. Po-Wen Chi

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei City 116, Taiwan
Interests: Applied Cryptography, Network and system security, Software defined networking, Network function virtualization, Embedded system development.

Special Issue Information

Dear Colleagues,

With the evolution of computer science, we have witnessed the numerous possibilities that one can produce. Creating something with the help of internet access has become more common among households; however, at the same time, personal information being stolen or systems being shut down by adversaries has become more frequent due to the increased usage of the internet. Thus, hackers prove that the internet is not perfect by launching brand new cyberattacks again and again. Many companies including large enterprises, such as Amazon, Google and Facebook, have encountered system breaches or had their own personal information stolen by hackers. Even the most prestigious companies have experienced information security exposure.

Therefore, information security is becoming increasingly important. Discovering and sharing new trends regarding information security is urgent, but also challenging.

This Special Issue aims to develop novel security mechanisms and security solutions for new applications in various novel environments.

Dr. Jheng-Jia Huang
Prof. Dr. Ray-Lin Tso
Dr. Po-Wen Chi
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.

Keywords

  • information security
  • network security
  • security application
  • security mechanisms
  • security solutions

Published Papers (3 papers)

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Research

20 pages, 1584 KiB  
Article
Numerical Feature Selection and Hyperbolic Tangent Feature Scaling in Machine Learning-Based Detection of Anomalies in the Computer Network Behavior
by Danijela Protić, Miomir Stanković, Radomir Prodanović, Ivan Vulić, Goran M. Stojanović, Mitar Simić, Gordana Ostojić and Stevan Stankovski
Electronics 2023, 12(19), 4158; https://doi.org/10.3390/electronics12194158 - 7 Oct 2023
Cited by 2 | Viewed by 897
Abstract
Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems [...] Read more.
Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm. Full article
(This article belongs to the Special Issue New Trends in Information Security)
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22 pages, 655 KiB  
Article
Automated Context-Aware Vulnerability Risk Management for Patch Prioritization
by Vida Ahmadi Mehri, Patrik Arlos and Emiliano Casalicchio
Electronics 2022, 11(21), 3580; https://doi.org/10.3390/electronics11213580 - 2 Nov 2022
Cited by 1 | Viewed by 1933
Abstract
The information-security landscape continuously evolves by discovering new vulnerabilities daily and sophisticated exploit tools. Vulnerability risk management (VRM) is the most crucial cyber defense to eliminate attack surfaces in IT environments. VRM is a cyclical practice of identifying, classifying, evaluating, and remediating vulnerabilities. [...] Read more.
The information-security landscape continuously evolves by discovering new vulnerabilities daily and sophisticated exploit tools. Vulnerability risk management (VRM) is the most crucial cyber defense to eliminate attack surfaces in IT environments. VRM is a cyclical practice of identifying, classifying, evaluating, and remediating vulnerabilities. The evaluation stage of VRM is neither automated nor cost-effective, as it demands great manual administrative efforts to prioritize the patch. Therefore, there is an urgent need to improve the VRM procedure by automating the entire VRM cycle in the context of a given organization. The authors propose automated context-aware VRM (ACVRM), to address the above challenges. This study defines the criteria to consider in the evaluation stage of ACVRM to prioritize the patching. Moreover, patch prioritization is customized in an organization’s context by allowing the organization to select the vulnerability management mode and weigh the selected criteria. Specifically, this study considers four vulnerability evaluation cases: (i) evaluation criteria are weighted homogeneously; (ii) attack complexity and availability are not considered important criteria; (iii) the security score is the only important criteria considered; and (iv) criteria are weighted based on the organization’s risk appetite. The result verifies the proposed solution’s efficiency compared with the Rudder vulnerability management tool (CVE-plugin). While Rudder produces a ranking independent from the scenario, ACVRM can sort vulnerabilities according to the organization’s criteria and context. Moreover, while Rudder randomly sorts vulnerabilities with the same patch score, ACVRM sorts them according to their age, giving a higher security score to older publicly known vulnerabilities. Full article
(This article belongs to the Special Issue New Trends in Information Security)
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25 pages, 5232 KiB  
Article
AIBot: A Novel Botnet Capable of Performing Distributed Artificial Intelligence Computing
by Hao Zhao, Hui Shu, Yuyao Huang and Ju Yang
Electronics 2022, 11(19), 3241; https://doi.org/10.3390/electronics11193241 - 9 Oct 2022
Viewed by 2703
Abstract
As an infrastructure platform for launching large-scale cyber attacks, botnets are one of the biggest threats to cyberspace security today. With the development of network technology and changes in the network environment, network attack intelligence has become a trend, and botnet designers are [...] Read more.
As an infrastructure platform for launching large-scale cyber attacks, botnets are one of the biggest threats to cyberspace security today. With the development of network technology and changes in the network environment, network attack intelligence has become a trend, and botnet designers are also committed to developing more destructive intelligent botnets. The feasibility of implementing distributed intelligent computing based on botnet node resources is analyzed with regard to the aspects of program size, communication traffic and resource occupancy. AIBot, a botnet model that can perform intelligent computation in a distributed manner, is proposed from the attacker’s perspective, which hierarchically deploys distributed neural network models in the botnet, thereby organizing nodes to collaboratively perform intelligent computation tasks. AIBot enables the distributed execution of intelligent computing tasks on a cluster of bot nodes by decomposing the computational load of a deep neural network model. A general algorithm for the distributed deployment of neural networks in AIBot is proposed, and the overall operational framework for AIBot is given. Two classical neural network models, CNN and RNN, are used as examples to illustrate specific schemes for deploying and running distributed intelligent computing in AIBot. Experimental scenarios were constructed to experimentally validate and briefly evaluate the performance of the two AIBot attack modes, and the overall efficiency of AIBot was evaluated in terms of execution time. This paper studies new forms of botnet attack techniques from a predictive perspective, aiming to increase defenders’ understanding of potential botnet threats, in order to propose effective defense strategies and improve the botnet defense system. Full article
(This article belongs to the Special Issue New Trends in Information Security)
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