AI-Driven Security and Privacy for IIoT Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 40
Special Issue Editor
Interests: IoT security; digital forensics; blockchain; privacy protection; information security
Special Issue Information
Dear Colleagues,
The rapid convergence of Artificial Intelligence (AI) and the Industrial Internet of Things (IIoT) is driving unprecedented advancements across various sectors, including healthcare, smart cities, industrial automation, and more. However, this convergence also introduces complex security and privacy challenges that require innovative solutions. As the number of IoT-connected devices continues to increase exponentially, so do the cybersecurity challenges due to the natural diversity of the IIoT ecosystem, limited hardware resources, and insufficient security capabilities. Intrusion detection systems (IDSs) are crucial in securing IIoT networks, and recent research has focused heavily on leveraging machine learning (ML) and deep learning (DL) to enhance these systems.
While ML and DL methods have proven effective in certain aspects, their performance heavily relies on the availability of large training datasets. This reliance poses a significant challenge in IIoT environments, where collecting vast amounts of data is often impractical. Furthermore, these methods may struggle to detect novel attacks, leading to vulnerabilities in IIoT networks. To address these limitations, few-shot learning (FSL) and zero-shot learning (ZSL) have emerged as promising approaches. FSL is capable of learning from a minimal number of training samples, while ZSL can generalize to new, unseen classes without requiring labeled training data. Despite their potential, the application of FSL and ZSL in IIoT security, particularly in IDSs, remains underexplored.
This Special Issue aims to explore the intersection of AI and IIoT, with a particular emphasis on how AI-driven approaches, including FSL and ZSL, can enhance the security and privacy of IIoT systems. We invite contributions that address both AI's vulnerabilities in IIoT environments and its potential to fortify these environments against evolving cyber threats, with a focus on overcoming the data scarcity challenges inherent in IIoT security. The topics of interest include, but are not limited to, the following:
- Few-shot learning for efficient IIoT intrusion detection;
- Zero-shot learning for novel threat detection in IIoT;
- AI-driven methods for anomaly detection and threat mitigation in IIoT networks;
- Adversarial machine learning attacks and defenses in IIoT systems;
- Addressing IIoT security data scarcity using few/zero-shot learning techniques;
- Comparative analysis of few/zero-shot learning versus traditional machine learning and deep learning in IIoT;
- Developing novel few/zero-shot learning models for IoT environments with limited data and novel threats;
- Robustness and resistance of AI models against random and adversarial perturbations in IIoT networks;
- Federated learning and other privacy-preserving AI techniques for IIoT data processing;
- AI-driven security solutions in smart cities, healthcare IoT, industrial IIoT, and other domains.
Prof. Dr. Jong Hyuk Park
Guest Editor
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Keywords
- IIoT security
- Few-shot learning
- Zero-shot learning
- Intrusion detection systems
- Data scarcity
- Machine learning
- Adversarial threats
- Privacy-preserving AI.
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