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Intrusion Detection Systems for Broadband Wireless Sensor Networks
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Intrusion Detection Systems for Broadband Wireless Sensor Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3315

Special Issue Editor

Prof. Dr. Cajetan M. Akujuobi

E-Mail Website
Guest Editor
Center of Excellence for Communication Systems Technology Research, Prairie View A&M University, Prairie View, TX 77446, USA
Interests: signal/image/video processing and communication systems; intrusion detection systems for broadband sensor networks; wavelets and wavelet transforms analysis and applications; cybersecurity; artificial intelligence and machine learning; smart technologies for smart and connected cities; broadband (high-speed) communication systems; multispectral image analysis using wavelets

Special Issue Information

Dear Colleagues,

Broadband wireless sensor networks are one of the most promising technologies, and have very important applications ranging from communication systems to health care and tactical military systems. Although broadband wireless sensor networks (BWSNs) have appealing features (e.g., low installation cost, unattended network operation), due to the lack of a physical line of defense (i.e., there are no gateways or switches to monitor the information flow) the security of such networks is a major concern, especially for applications where confidentiality has prime importance. Therefore, in order to operate BWSNs securely, any kind of intrusions should be detected before attackers can harm the network (i.e., sensor nodes) and/or information destination (i.e., data sink or base station). In this Special Issue, we are inviting contributions on the latest advances and developments of algorithms, schemes, and architectures for intrusion detection systems for broadband wireless sensor networks.

Topics to be covered include, but are not limited to the following:

  • Intrusion detection systems in broadband wireless sensor networks.
  • Algorithms for intrusion detection systems for broadband wireless sensor networks.
  • Classifiers for network intrusion detection systems for broadband sensor networks.
  • Denial of service for resource availability for broadband wireless sensor networks.
  • Intrusion detection mechanisms for broadband wireless sensor networks in smart environments.
  • Generating a new intrusion detection datasets and intrusion traffic characterization.
  • Broadband-sensor-network-based intrusion detection datasets.
  • Deep learning for cybersecurity intrusion detection broadband sensor networks.
  • Anonymous channel categorization scheme of edge nodes to detect jamming attacks in broadband sensor networks.
  • Broadband sensor networks using protocol layer trust-based intrusion detection systems.
  • Design of broadband sensor networks with predictable performances.
  • Intruder detection for sinkhole attack in broadband wireless sensor networks.
  • Intrusion detection systems in broadband wireless sensor networks using conditioning generative adversarial network.
  • Machine learning and deep learning methods for intrusion detection systems for broadband sensor networks.
  • Comparative analysis for intrusion detection systems for broadband sensor networks.

Prof. Dr. Cajetan M. Akujuobi
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. Sensors 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 2600 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

  • broadband sensor networks
  • intrusion detection
  • intrusion detection algorithms
  • wireless sensor networks
  • network security and algorithms
  • denial of service for sensor networks
  • machine learning with application to broadband sensor networks
  • deep learning with application to broadband sensor networks
  • datasets for sensor networks

Published Papers (2 papers)

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Research

16 pages, 1095 KiB  
Article
Data-Driven Network Analysis for Anomaly Traffic Detection
by Shumon Alam, Yasin Alam, Suxia Cui and Cajetan Akujuobi
Sensors 2023, 23(19), 8174; https://doi.org/10.3390/s23198174 - 29 Sep 2023
Viewed by 1101
Abstract
Cybersecurity is a critical issue in today’s internet world. Classical security systems, such as firewalls based on signature detection, cannot detect today’s sophisticated zero-day attacks. Machine learning (ML) based solutions are more attractive for their capabilities of detecting anomaly traffic from benign traffic, [...] Read more.
Cybersecurity is a critical issue in today’s internet world. Classical security systems, such as firewalls based on signature detection, cannot detect today’s sophisticated zero-day attacks. Machine learning (ML) based solutions are more attractive for their capabilities of detecting anomaly traffic from benign traffic, but to develop an ML-based anomaly detection system, we need meaningful or realistic network datasets to train the detection engine. There are many public network datasets for ML applications. Still, they have limitations, such as the data creation process and the lack of diverse attack scenarios or background traffic. To create a good detection engine, we need a realistic dataset with various attack scenarios and various types of background traffic, such as HTTPs, streaming, and SMTP traffic. In this work, we have developed realistic network data or datasets considering various attack scenarios and diverse background/benign traffic. Furthermore, considering the importance of distributed denial of service (DDoS) attacks, we have compared the performance of detecting anomaly traffic of some classical supervised and our prior developed unsupervised ML algorithms based on the convolutional neural network (CNN) and pseudo auto-encoder (AE) architecture based on the created datasets. The results show that the performance of the CNN-Pseudo-AE is comparable to that of many classical supervised algorithms. Hence, the CNN-Pseudo-AE algorithm is promising in actual implementation. Full article
(This article belongs to the Special Issue Intrusion Detection Systems for Broadband Wireless Sensor Networks)
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25 pages, 965 KiB  
Article
Wireless Local Area Networks Threat Detection Using 1D-CNN
by Marek Natkaniec and Marcin Bednarz
Sensors 2023, 23(12), 5507; https://doi.org/10.3390/s23125507 - 12 Jun 2023
Cited by 5 | Viewed by 1687
Abstract
Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio [...] Read more.
Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks. Full article
(This article belongs to the Special Issue Intrusion Detection Systems for Broadband Wireless Sensor Networks)
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