Navigating AI Challenges in Cybersecurity: Strategies for Industrial and IoT Protection

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

Deadline for manuscript submissions: 15 October 2024 | Viewed by 288

Special Issue Editors


E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Interests: Internet of Things security; network security; intrusion detection; machine learning; neural networks

E-Mail Website
Guest Editor
School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK
Interests: machine learning; adversarial machine learning; intrusion detection; text mining

Special Issue Information

Dear Colleagues,

This Special Issue tackles pressing challenges in cybersecurity, with a specific focus on protecting industrial control systems and Internet of Things (IoT) devices in the rapidly evolving domain of machine learning and AI applications. A pivotal concern is the resilience of AI-driven systems to the complexities of Adversarial Machine Learning (AML) and the phenomenon of concept drift. While these factors are not direct cyber-attacks, they significantly undermine the effectiveness and dependability of AI models used in cybersecurity. This can lead to severe cybersecurity repercussions, as vulnerabilities in AI can be exploited to compromise the security and integrity of critical systems. Understanding and mitigating these AI-related issues is crucial for maintaining robust cybersecurity defenses in our increasingly interconnected and AI-dependent world.

Areas of focus encompass, but are not limited to, the following:

  • Robust and adaptive machine learning techniques that maintain effectiveness in the face of AML strategies, where models are probed and exploited by adversaries.
  • Strategies for AI systems in ICS and IoT to effectively adapt to concept drift, where the change in data patterns over time can diminish the accuracy and reliability of machine learning models.
  • Evaluations of the vulnerabilities of current AI-based cybersecurity models in industrial and IoT settings, with a focus on their resilience to AML and concept drift.
  • Real-world case studies showcasing the implementation of machine learning solutions that effectively address AML and concept drift in industrial and IoT scenarios.
  • Theoretical and empirical research on the intersection of machine learning, cybersecurity, and the dynamic challenges posed by AML and concept drift.

Dr. Eirini Anthi
Dr. Lowri Williams
Guest Editors

Manuscript Submission Information

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Keywords

  • cybersecurity
  • industrial control systems (ICS)
  • internet of things (IoT)
  • adversarial machine learning (AML)
  • concept drift
  • AI vulnerabilities
  • machine learning security
  • cyber threats
  • AI in cybersecurity
  • system resilience

Published Papers

This special issue is now open for submission.
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