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Electronics
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Cyber Attacks: Threats and Security Solutions
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Cyber Attacks: Threats and Security Solutions

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 22080

Special Issue Editor

Prof. Dr. Ankit Chaudhary

E-Mail Website
Guest Editor
Department of Computer Science, The University of Missouri, Columbia, MO 63121, USA
Interests: cyber security; data sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, the world is connect through cyber networks and cyber-crime is observed at a very high rate. The security of industrial plants and safe financial transactions are minimum requirements for security and privacy. Additionally, intrusion, data poisoning, and port bombarding are common issues in the cyber-world. Cyber-attackers can be hired these days for a small compensation and can target anyone to disturb the running system. These attack can be country-specific or issue-specific with the aim of hitting a target. Cyber-attacks were at an all-time high even during the pandemic, continuing this trend today. The capability to detect, stop and defend against such crimes and fraud is a challenge and the law must support for agencies in the pursuit of cyber-attackers.

Prof. Dr. Ankit Chaudhary
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

  • detection and analysis of advanced cyber-attacks, techniques, and procedures
  • malware analysis
  • analytic techniques for the detection and analysis of cyber-crime
  • application of machine-learning tools and techniques in cyber-crime
  • theories and models for the detection and analysis of advanced persistent threats
  • automated and smart tools for the collection, preservation, and analysis of digital evidence
  • cyber-law and policies
  • threat intelligence techniques for reacting to advanced intrusion campaigns
  • applying machine learning tools and techniques for malware analysis and fighting against cyber-crimes
  • intelligent forensic tools, techniques, and procedures for cloud, mobile, and data-center forensics
  • intelligent analysis of different types of data collected from different layers of network security solutions
  • intelligent methods to manage, share, and receive logs and data relevant to a variety of adversary groups
  • interpretation of cyber data utilizing intelligent data analysis techniques
  • infer intelligence of existing cyber security data generated using different monitoring and defense solutions
  • automated and intelligent methods for adversary profiling

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Published Papers (4 papers)

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Research

17 pages, 1725 KiB  
Article
Enhancing Cyber-Threat Intelligence in the Arab World: Leveraging IoC and MISP Integration
by Ibrahim Yahya Alzahrani, Seokhee Lee and Kyounggon Kim
Electronics 2024, 13(13), 2526; https://doi.org/10.3390/electronics13132526 - 27 Jun 2024
Cited by 1 | Viewed by 17831
Abstract
Cybercrime threat intelligence enables proactive measures against threat actors and informed, data-driven security decisions. This study proposes a practical implementation of cybercrime threat intelligence in the Arab world by integrating Indicators of Compromise and collecting security alerts from honeypot systems and open-source intelligence. [...] Read more.
Cybercrime threat intelligence enables proactive measures against threat actors and informed, data-driven security decisions. This study proposes a practical implementation of cybercrime threat intelligence in the Arab world by integrating Indicators of Compromise and collecting security alerts from honeypot systems and open-source intelligence. The data collected are stored on the Malware Information Sharing Platform, an open-source platform used to create and share Indicators of Compromise. This study highlights the intuitive interface of the Malware Information Sharing Platform for data analysis, threat identification, and the correlation of Indicators of Compromise. In addition, machine learning techniques are applied to improve predictive accuracy and identify patterns in the data. The decision tree classifier achieves a high accuracy of 99.79%, and the results reveal significant potential cyber-threats, demonstrating the effectiveness of the platform in providing actionable information to prevent, detect, and respond to cybercrime. This approach aims to improve the security posture of the Arab region. Full article
(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
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34 pages, 20870 KiB  
Article
To (US)Be or Not to (US)Be: Discovering Malicious USB Peripherals through Neural Network-Driven Power Analysis
by Koffi Anderson Koffi, Christos Smiliotopoulos, Constantinos Kolias and Georgios Kambourakis
Electronics 2024, 13(11), 2117; https://doi.org/10.3390/electronics13112117 - 29 May 2024
Viewed by 1228
Abstract
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the [...] Read more.
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score. Full article
(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
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15 pages, 267 KiB  
Article
Advanced Algorithmic Approaches for Scam Profile Detection on Instagram
by Biodoumoye George Bokolo and Qingzhong Liu
Electronics 2024, 13(8), 1571; https://doi.org/10.3390/electronics13081571 - 19 Apr 2024
Viewed by 1321
Abstract
Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. [...] Read more.
Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception. Full article
(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
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17 pages, 462 KiB  
Article
Multi-Dimensional Moving Target Defense Method Based on Adaptive Simulated Annealing Genetic Algorithm
by Hanyi Xu, Guozhen Cheng, Xiaohan Yang, Wenyan Liu, Dacheng Zhou and Wei Guo
Electronics 2024, 13(3), 487; https://doi.org/10.3390/electronics13030487 - 24 Jan 2024
Viewed by 1032
Abstract
Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated [...] Read more.
Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated annealing genetic algorithm (MD2RS) is proposed. Firstly, according to the characteristics of microservices in the cloud, a microservice attack graph is proposed to quantify the attack scenario of microservices in the cloud so as to conveniently and intuitively observe the vulnerability of microservices in the cloud and the dependency relationship between microservices. Secondly, the security gain and resource cost are quantified for the key nodes selected by measuring the degree of dependence of each node according to the degree centrality. Finally, the Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is used to solve the optimal security configuration information of the moving target defense, that is, the combination of the number of copies of the multi-copy deployment and the rotation cycle of the dynamic rotation of microservices, in order to quickly evaluate the security risks of microservices and optimize the security policy. Experiments show that the defense return rate of MD2RS is 85.95% higher than that of the mainstream methods, and the experimental results are conducive to applying this method to the dynamic defense of microservices in the cloud. Full article
(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
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