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Electronics
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AI in Cybersecurity, Volume II
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AI in Cybersecurity, Volume II

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2693

Special Issue Editors

Dr. Ayush Goyal

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Interests: computer vision; machine learning; artificial intelligence; pattern recognition; biomedical engineering; biomedical signal and image processing; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Avdesh Mishra

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Interests: bioinformatics; computational biology; machine learning; pattern recognition; data mining and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Mais Nijim

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Interests: parallel distributed systems; networking; storage systems; cluster and grid computing; real-time systems; fault-tolerant computing; performance evaluation; dynamic resource management; network security
Special Issues, Collections and Topics in MDPI journals
Dr. David Hicks

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Interests: object-oriented programming; mobile development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cyber defense and security is now an essential field of computer science in light of the ever-increasing threats and attacks on computer infrastructure. Machine learning and artificial intelligence methods are applicable in the detection of cyber threats, such as malware analysis, intrusion detection, injection attacks, etc. There are various algorithms in machine learning and artificial intelligence methods. Additionally, there are several applications of cyber defense, including firewall configuration, packet sniffing, network analysis, and network traffic monitoring. This Special Issue welcomes papers on any of these above mentioned or related topics using or developing machine learning and artificial intelligence algorithms.

Dr. Ayush Goyal
Dr. Avdesh Mishra
Dr. Mais Nijim
Dr. David Hicks
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

  • cybersecurity
  • cyber defense
  • cyber intelligence
  • machine learning
  • artificial intelligence
  • intrusion detection
  • malware analysis

Related Special Issue

Published Papers (2 papers)

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21 pages, 1524 KiB  
Article
An Ensemble of Text Convolutional Neural Networks and Multi-Head Attention Layers for Classifying Threats in Network Packets
by Hyeonmin Kim and Young Yoon
Electronics 2023, 12(20), 4253; https://doi.org/10.3390/electronics12204253 - 14 Oct 2023
Cited by 2 | Viewed by 934
Abstract
Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial [...] Read more.
Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads that contain binary and text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte-pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets such as IP addresses and ports. Given these features, we combine a text 1D CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique on the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC), currently operating in South Korea. The proposed model showed remarkable performance compared to previous studies, achieving highly accurate classification with an F1-score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute, helping security agents in the field maximize their time and analyze more complex attack patterns. Full article
(This article belongs to the Special Issue AI in Cybersecurity, Volume II)
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Review

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18 pages, 640 KiB  
Review
A Review of Deep Learning-Based Binary Code Similarity Analysis
by Jiang Du, Qiang Wei, Yisen Wang and Xiangjie Sun
Electronics 2023, 12(22), 4671; https://doi.org/10.3390/electronics12224671 - 16 Nov 2023
Viewed by 1355
Abstract
Against the backdrop of highly developed software engineering, code reuse has been widely recognized as an effective strategy to significantly alleviate the burden of development and enhance productivity. However, improper code citation could lead to security risks and license issues. With the source [...] Read more.
Against the backdrop of highly developed software engineering, code reuse has been widely recognized as an effective strategy to significantly alleviate the burden of development and enhance productivity. However, improper code citation could lead to security risks and license issues. With the source codes of many pieces of software being difficult to obtain, binary code similarity analysis (BCSA) has been extensively implemented in fields such as bug search, code clone detection, and patch analysis. This research selects 39 papers on BCSA from top-tier and emerging conferences within artificial intelligence, network security, and software engineering from 2016 to 2022 for deep analysis. The central focus lies on methods utilizing deep learning technologies, detailing a thorough summary and the arrangement of the application and implementation specifics of various deep learning technologies. Furthermore, this study summarizes the research patterns and development trends in this field, thereby proposing potential directions for future research. Full article
(This article belongs to the Special Issue AI in Cybersecurity, Volume II)
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