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Electronics
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Recent Advances in Intrusion Detection Systems Using Machine Learning
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Recent Advances in Intrusion Detection Systems Using Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2971

Special Issue Editors

Dr. Sicong Shao

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
Interests: cybersecurity; machine learning; social cybersecurity; social computing; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jielun Zhang

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
Interests: network security; networking analytics; cybersecurity; Internet-of-Things

Special Issue Information

Dear Colleagues,

Cyber-attacks are not only increasing but are also evolving rapidly to become highly sophisticated, thereby leading to increasing challenges in precisely detecting threats and intrusions, making the development of advanced intrusion detection systems (IDSs) more crucial than ever. Accordingly, numerous IDSs are designed and used to protect valuable assets in financial services, healthcare, manufacturing, data centers, critical infrastructures, etc. Many research ideas targeting IDSs using artificial intelligence (AI) and machine learning (ML) techniques have been proposed. Particularly, in recent years, IDSs leveraging deep learning have demonstrated remarkable capabilities in learning representations of complex data, ranging from high-dimensional to temporal and spatial data, pushing the frontiers of these systems.  While successful in many domains, IDSs still suffer from many issues. Some of them are as follows: (1) False-positive rates are high. (2) Security experts need to conduct elaborate feature extraction. (3) Insufficient data can be used to train effective models in some sensitive applications. (4) The detection performance decreases over time due to concept drift. This Special Issue aims to address the challenges and present innovative techniques in the field. Both original research papers and reviews are welcome. Research may focus on (but is not limited to) the following topics:

  • Deep learning for IDSs;
  • Federated learning for intrusion detection ;
  • Anomaly detection for IDSs;
  • Concept drift in IDSs;
  • Adaptive learning for IDSs;
  • Network-based IDSs using AI/ML;
  • Host-based IDSs using AI/ML;
  • IDSs using AI/ML for cyber-physical systems;
  • IDSs using AI/ML for IoT/IIoT;
  • Privacy and trust in IDSs;
  • Privacy preservation techniques in IDSs;
  • Large-scale distributed intrusion detection.

Dr. Sicong Shao
Dr. Jielun Zhang
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

  • intrusion detection
  • system and software security
  • network security
  • machine learning
  • artificial intelligence
  • deep learning

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Published Papers (2 papers)

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Research

19 pages, 6762 KiB  
Article
Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data
by Akshay Ram Ramchandra, Anton Skurdal, Prakash Ranganathan and William Semke
Electronics 2024, 13(16), 3145; https://doi.org/10.3390/electronics13163145 - 8 Aug 2024
Viewed by 1008
Abstract
This paper investigates the detection of Global Positioning System (GPS) interference during the Dallas Fort Worth (DFW) event from 17 to 19 October 2022, utilizing various machine learning (ML) models. The study examines the effectiveness of several ML models, including neural networks (NN), [...] Read more.
This paper investigates the detection of Global Positioning System (GPS) interference during the Dallas Fort Worth (DFW) event from 17 to 19 October 2022, utilizing various machine learning (ML) models. The study examines the effectiveness of several ML models, including neural networks (NN), tree-based models, regression-based models, Bayesian classifiers, distance-based models, and stochastic classifiers, in identifying GPS interference. A simulated training signature was created with 180,000 data points, of which 25,792 were modeled as positive samples indicating GPS interference. Preliminary results reveal that the Multi-Layer Perceptron (MLP) model outperformed others, achieving a 99.8% True Positive Rate (TPR). Additionally, permutation feature importance was utilized to understand how model feature prioritization impacts the detection outcomes. Given the increasing frequency of GPS interference, these findings underscore the critical importance of ML techniques in detecting GPS interference patterns in Automatic Dependent Surveillance-Broadcast (ADS-B) data. Full article
(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)
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22 pages, 3698 KiB  
Article
An Email Cyber Threat Intelligence Method Using Domain Ontology and Machine Learning
by Algimantas Venčkauskas, Jevgenijus Toldinas, Nerijus Morkevičius and Filippo Sanfilippo
Electronics 2024, 13(14), 2716; https://doi.org/10.3390/electronics13142716 - 11 Jul 2024
Viewed by 1108
Abstract
Email is an excellent technique for connecting users at low cost. Spam emails pose the risk of collecting a user’s personal information by fooling them into clicking on a link or engaging in other fraudulent activities. Furthermore, when a spam message is delivered, [...] Read more.
Email is an excellent technique for connecting users at low cost. Spam emails pose the risk of collecting a user’s personal information by fooling them into clicking on a link or engaging in other fraudulent activities. Furthermore, when a spam message is delivered, the user may read the entire message before deciding it is spam and deleting it. Most approaches to email classification proposed by other authors use natural language processing (NLP) methods to analyze the content of email messages. One of the biggest shortcomings of NLP-based methods is their dependence on the language in which a message is written. To construct an effective email cyber threat intelligence (CTI) sharing framework, the privacy of a message’s content must be preserved. This article proposes a novel domain-specific ontology and method for emails that require only the metadata of email messages to be shared to preserve their privacy, making them applicable to solutions for sharing email CTI. To preserve privacy, a new semantic parser was developed for the proposed email domain-specific ontology to populate email metadata and create a dataset. Machine learning algorithms were examined, and experiments were conducted to identify and classify spam messages using the newly created dataset. Feature-ranking algorithms, chi-squared, ANOVA (analysis of variance), and Kruskal–Wallis tests were used. In all experiments, the kernel naïve Bayes model demonstrated acceptable results. The highest accuracy of 92.28% and an F1 score of 95.92% for recognizing spam email messages were obtained using the proposed domain-specific ontology, the newly developed semantic parser, and the created metadata dataset. Full article
(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)
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