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Information
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Intrusion Detection Systems in IoT Networks
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Intrusion Detection Systems in IoT Networks

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".

Deadline for manuscript submissions: 13 March 2025 | Viewed by 5570

Special Issue Editor

Dr. Jian Tang

E-Mail Website
Guest Editor
Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada
Interests: machine learning; data mining; IDS in IoT

Special Issue Information

Dear Colleagues,

We are pleased to announce a forthcoming Special Issue, upcoming for publication in the journal of Information. We aim to cover a broad range of subjects related to intrusion detection systems (IDS) in IoT networks, and hope that we will be joined in this endeavor by research leaders in this field.

IoT is a type of network that connects all sorts of different applications based
on the convergence of smart objects and the Internet. In recent years, IoT has penetrated every aspect of life, including the human body, home, and the living environment. Moreover, it is increasingly being adapted to a higher level of usage by various private and public sectors, such as military and nuclear facilities, civilian institutions/organizations, and governmental bodies. The aim is to improve their capacity in terms of environmental management, decision making, intelligence, among other goals. Such vast and vital applications come with security risks and threats.

An IoT network provides powerful computations, as well as valuable and sensitive data collected from interconnected devices within the corresponding IoT network. These devices are usually developed based on specific types of functionalities, which are based in turn on low cost and limited resources in computational capabilities, power, and storage. As a result, such networks are vulnerable to a wide array of security threats. Securing IoT networks is vital due to the importance and sensitivity of the data they collect.

The nature of IoT networks, such as interconnecting a large number of devices with limited resources, and heterogeneity between various IoT networks, raises security challenges. As a result, traditional security methods, such as cryptography, are less effective against IoT cyberattacks. Accordingly, different methodologies and technologies are needed for detecting cyberattacks in IoT networks.

We invite researchers to submit scholarly articles on a range of topics including, but not limited to the following topics:

  • Data confidentiality and authentication in IoT networks
  • Access control within the IoT networks
  • Privacy and trust among users and things
  • Enforcement of security and privacy policies
  • Strategies for hosting IDS agents in IoT devices
  • Methodologies of adapting IDS to IoT architectures
  • Taxonomy of IDS for IoT networks
  • Placement of IDS in IoT networks
  • Comparison of different IDSs
  • Validation strategies
  • Secure alert traffic and management
  • Network administrations for IoT in the presence of IDS
  • Autonomic IDSs
  • Incorporating machine learning strategies for IDS in IoT networks

Dr. Jian Tang
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. Information is an international peer-reviewed open access monthly 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 1600 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.

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Published Papers (3 papers)

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20 pages, 3271 KiB  
Article
Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model
by Mostafa Mahmoud El-Gayar, Faheed A. F. Alrslani and Shaker El-Sappagh
Information 2024, 15(10), 583; https://doi.org/10.3390/info15100583 - 24 Sep 2024
Viewed by 833
Abstract
The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this [...] Read more.
The advent of the Fourth Industrial Revolution has positioned the Internet of Things as a pivotal force in intelligent vehicles. With the source of vehicle-to-everything (V2X), Internet of Things (IoT) networks, and inter-vehicle communication, intelligent connected vehicles are at the forefront of this transformation, leading to complex vehicular networks that are crucial yet susceptible to cyber threats. The complexity and openness of these networks expose them to a plethora of cyber-attacks, from passive eavesdropping to active disruptions like Denial of Service and Sybil attacks. These not only compromise the safety and efficiency of vehicular networks but also pose a significant risk to the stability and resilience of the Internet of Vehicles. Addressing these vulnerabilities, this paper proposes a Dynamic Forest-Structured Ensemble Network (DFSENet) specifically tailored for the Internet of Vehicles (IoV). By leveraging data-balancing techniques and dimensionality reduction, the DFSENet model is designed to detect a wide range of cyber threats effectively. The proposed model demonstrates high efficacy, with an accuracy of 99.2% on the CICIDS dataset and 98% on the car-hacking dataset. The precision, recall, and f-measure metrics stand at 95.6%, 98.8%, and 96.9%, respectively, establishing the DFSENet model as a robust solution for securing the IoV against cyber-attacks. Full article
(This article belongs to the Special Issue Intrusion Detection Systems in IoT Networks)
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20 pages, 2708 KiB  
Article
Investigating Credit Card Payment Fraud with Detection Methods Using Advanced Machine Learning
by Victor Chang, Basit Ali, Lewis Golightly, Meghana Ashok Ganatra and Muhidin Mohamed
Information 2024, 15(8), 478; https://doi.org/10.3390/info15080478 - 12 Aug 2024
Viewed by 3066
Abstract
In the cybersecurity industry, where legitimate transactions far outnumber fraudulent ones, detecting fraud is of paramount significance. In order to evaluate the accuracy of detecting fraudulent transactions in imbalanced real datasets, this study compares the efficacy of two approaches, random under-sampling and oversampling, [...] Read more.
In the cybersecurity industry, where legitimate transactions far outnumber fraudulent ones, detecting fraud is of paramount significance. In order to evaluate the accuracy of detecting fraudulent transactions in imbalanced real datasets, this study compares the efficacy of two approaches, random under-sampling and oversampling, using the synthetic minority over-sampling technique (SMOTE). Random under-sampling aims for fairness by excluding examples from the majority class, but this compromises precision in favor of recall. To strike a balance and ensure statistical significance, SMOTE was used instead to produce artificial examples of the minority class. Based on the data obtained, it is clear that random under-sampling achieves high recall (92.86%) at the expense of low precision, whereas SMOTE achieves a higher accuracy (86.75%) and a more even F1 score (73.47%) at the expense of a slightly lower recall. As true fraudulent transactions require at least two methods for verification, we investigated different machine learning methods and made suitable balances between accuracy, F1 score, and recall. Our comparison sheds light on the subtleties and ramifications of each approach, allowing professionals in the field of cybersecurity to better choose the approach that best meets the needs of their own firm. This research highlights the need to resolve class imbalances for effective fraud detection in cybersecurity, as well as the need for constant monitoring and the investigation of new approaches to increase applicability. Full article
(This article belongs to the Special Issue Intrusion Detection Systems in IoT Networks)
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15 pages, 2488 KiB  
Article
Extended Isolation Forest for Intrusion Detection in Zeek Data
by Fariha Moomtaheen, Sikha S. Bagui, Subhash C. Bagui and Dustin Mink
Information 2024, 15(7), 404; https://doi.org/10.3390/info15070404 - 12 Jul 2024
Viewed by 1027
Abstract
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Isolation Forest algorithm, known for [...] Read more.
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Isolation Forest algorithm, known for its efficacy in detecting anomalies in high-dimensional data. Our research assesses the performance of the EIF model on a newly created dataset composed of Zeek Connection Logs, UWF-ZeekDataFall22. To handle the enormous volume of data involved in this research, the Hadoop Distributed File System (HDFS) is employed for efficient and fault-tolerant storage, and the Apache Spark framework, a powerful open-source Big Data analytics platform, is utilized for machine learning (ML) tasks. The best results for the EIF algorithm came from the 0-extension level. We received an accuracy of 82.3% for the Resource Development tactic, 82.21% for the Reconnaissance tactic, and 78.3% for the Discovery tactic. Full article
(This article belongs to the Special Issue Intrusion Detection Systems in IoT Networks)
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