Advances in Intelligent Defense Systems for the Internet of Things

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1343

Special Issue Editors


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Guest Editor
One of the World’s Top 2% Scientists, Department of Cybersecurity, Faculty of Computer & Information Technology, Jordan University of Science and Technology (JUST), P.O. Box 3030, Irbid 22110, Jordan
Interests: cryptography; intrusion detection systems; network security; artificial intelligence; malware analysis; the Internet of Things; reverse engineering
Department of Computer Science, King Hussein School of Computing Sciences, Prince Sumaya University for Technology, P.O. Box 1438, Amman 11941, Jordan
Interests: cybersecurity and cryptography; the Internet of Things (IoT); machine learning

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Guest Editor
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Interests: security of cyber–physical systems; autonomous vehicles; artificial intelligence; machine learning; deep learning; the IoMT; security of the IoT

E-Mail Website
Guest Editor
Data Science Research Centre, Graduate School, Edge Hill University, Ormskirk, Lancashire L39 4QP, UK
Interests: cloud computing; Internet of Things; social graphs; big data; future internet; resource provisioning; global challenges
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Special Issue Information

Dear Colleagues,

The proliferation of Internet of things (IoT) devices has introduced unprecedented connectivity and convenience into our lives, revolutionizing various sectors such as healthcare, transportation, manufacturing, and smart homes; however, this rapid expansion poses significant security challenges, as IoT devices often possess limited computational power and are deployed in diverse and dynamic environments. Addressing these challenges requires the development of intelligent defense systems capable of detecting, mitigating, and responding to a wide range of security threats in real time. 

This Special Issue aims to explore recent advances in intelligent defense systems tailored specifically for IoT environments. Topics of interest include, but are not limited to, the following:

  1. Threat detection and anomaly detection techniques for IoT networks.
  2. Machine learning and AI-based intrusion detection and prevention approaches in IoT systems.
  3. Secure communication protocols and encryption techniques for IoT devices.
  4. Adaptive and self-learning defense mechanisms for IoT networks.
  5. Privacy-preserving solutions for IoT data collection and processing.
  6. Hardware-level security mechanisms for IoT devices.
  7. Collaborative defense strategies leveraging edge computing and cloud resources.
  8. Case studies and real-world deployments of intelligent defense systems in IoT applications.
  9. Standardization and regulatory efforts for enhancing IoT security.
  10. Vulnerability assessment and penetration testing methodologies for IoT ecosystems. 

We welcome original research articles, reviews, and case studies addressing the above topics. Submissions should adhere to the journal's formatting and submission guidelines. All manuscripts will undergo a rigorous peer review process to ensure the quality and relevance of the contributions. 

Dr. Qasem Abu Al-Haija
Dr. Ammar Odeh
Dr. Abdulaziz Alsulami
Prof. Dr. Nik Bessis
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. Big Data and Cognitive Computing 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 1800 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

  • Internet of Things
  • intelligent defense systems
  • security

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Published Papers (1 paper)

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Research

23 pages, 3337 KiB  
Article
Attention-Driven Transfer Learning Model for Improved IoT Intrusion Detection
by Salma Abdelhamid, Islam Hegazy, Mostafa Aref and Mohamed Roushdy
Big Data Cogn. Comput. 2024, 8(9), 116; https://doi.org/10.3390/bdcc8090116 - 9 Sep 2024
Viewed by 949
Abstract
The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This [...] Read more.
The proliferation of Internet of Things (IoT) devices has become inevitable in contemporary life, significantly affecting myriad applications. Nevertheless, the pervasive use of heterogeneous IoT gadgets introduces vulnerabilities to malicious cyber-attacks, resulting in data breaches that jeopardize the network’s integrity and resilience. This study proposes an Intrusion Detection System (IDS) for IoT environments that leverages Transfer Learning (TL) and the Convolutional Block Attention Module (CBAM). We extensively evaluate four prominent pre-trained models, each integrated with an independent CBAM at the uppermost layer. Our methodology is validated using the BoT-IoT dataset, which undergoes preprocessing to rectify the imbalanced data distribution, eliminate redundancy, and reduce dimensionality. Subsequently, the tabular dataset is transformed into RGB images to enhance the interpretation of complex patterns. Our evaluation results demonstrate that integrating TL models with the CBAM significantly improves classification accuracy and reduces false-positive rates. Additionally, to further enhance the system performance, we employ an Ensemble Learning (EL) technique to aggregate predictions from the two best-performing models. The final findings prove that our TL-CBAM-EL model achieves superior performance, attaining an accuracy of 99.93% as well as high recall, precision, and F1-score. Henceforth, the proposed IDS is a robust and efficient solution for securing IoT networks. Full article
(This article belongs to the Special Issue Advances in Intelligent Defense Systems for the Internet of Things)
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