Securing Future Internet with Computational Intelligence

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4895

Special Issue Editors


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Guest Editor
Seneca College, North York, Toronto, ON M2J 2X5, Canada
Interests: cybersecurity; network security; IoT security; cryptography and applications of machine-learning in cybersecurity
Department of Computer Science, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Interests: autonomic computing; cloud/fog computing; IoT; federated learning; smart systems; energy efficiency

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Guest Editor
School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS16 5LF, UK
Interests: eHealth; IoT; Time-series prediction; predictive analytics
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Special Issue Information

Dear Colleagues,

With the rapid developments in Internet technologies and the wide adoption of the IoT and interconnected devices, security challenges arise rapidly as well. Malicious actors find new attack surfaces to target new technologies. These developing threads require innovative solutions that employ computational intelligence.

In this Special Issue, we would like to collect recent developments in state-of-the-art research in applications of computational intelligence in protecting the future Internet. Topics covered in this Special Issue include, but are not limited to, the following:

  • Computational intelligence applications in network security;
  • Computational intelligence applications in IoT and Industrial IoT security;
  • Computational intelligence applications in cybersecurity;
  • Computational intelligence applications in detecting APTs;
  • Computational intelligence applications in threat intelligence;
  • Computational intelligence applications in protecting IoMT;
  • Computational intelligence applications in vulnerability detection and management;
  • Computational intelligence applications in intrusion detection and prevention;
  • Protecting AI/ML against adversarial attacks.

Prof. Dr. Mohammed M. Alani
Dr. Thar Baker
Prof. Dr. Hissam Tawfik
Guest Editors

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Keywords

  • cybersecurity
  • computational intelligence
  • AI
  • Ml
  • IoT security
  • network security

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Published Papers (1 paper)

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Research

28 pages, 1016 KiB  
Article
An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
by Farhad Ahamed, Farnaz Farid, Basem Suleiman, Zohaib Jan, Luay A. Wahsheh and Seyed Shahrestani
Future Internet 2022, 14(8), 222; https://doi.org/10.3390/fi14080222 - 26 Jul 2022
Cited by 23 | Viewed by 4012
Abstract
With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in [...] Read more.
With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16. Full article
(This article belongs to the Special Issue Securing Future Internet with Computational Intelligence)
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