Topic: Security, Privacy, and Trustworthiness in Intelligent Cyber-Physical Systems, Internet of Things, and Large Language Models

The rapid evolution of Intelligent Cyber-Physical Systems (ICPS) and the Internet of Things (IoT) has redefined the landscape of smart cities, autonomous systems, and ubiquitous computing environments. However, as the capabilities of these systems increase, so do the associated vulnerabilities and threats. One significant driving force behind the surge in capabilities and complexities is the emergence of Large Language Models (LLMs), which are now extensively integrated into various applications, ranging from automated decision-making to user interactions. The fusion of LLMs with ICPS and IoT brings forth a unique set of challenges and opportunities. While LLMs can enhance the intelligence and adaptability of these systems, they also introduce new avenues for malicious attacks, data breaches, and concerns over trustworthiness. Addressing these challenges necessitates a multidisciplinary approach, combining the expertise of cyber-physical system designers, IoT architects, and AI researchers. This Special Issue aims to provide a platform for researchers and professionals to present their latest findings, developments, and visions in security, privacy, and trustworthiness for ICPS and IoT, especially when LLMs are in the picture. We invite high-quality and original research papers that address theoretical aspects, practical implementations, innovative applications, and case studies relevant to this nexus of technology.

Topics of interest include, but are not limited to:
● Protecting data integrity during transmission and storage;
● Edge computing security in ICPS and IoT deployments;
● Self-healing and adaptive security mechanisms;
● Building Trust Bridges: Ensuring Reliability and Transparency of AI within ICPS and IoT;
● Explainable AI (XAI) in cyber-physical systems;
● User-centric privacy controls in IoT devices;
● Verification and validation of AI models in real-time environments;
● Guarding the Gatekeepers: Security Measures Exclusive to LLMs;
● Protecting LLMs from data poisoning and backdoor attacks;
● Ethical considerations and bias detection in LLM outputs;
● Privacy-preserving techniques for LLMs: Differential privacy and federated learning;
● Battle Tactics: Navigating and Neutralizing Adversarial Onslaughts;
● Advanced detection techniques for zero-day exploits in ICPS and IoT;
● Countermeasures against adversarial machine learning attacks on LLMs;
● Real-time threat intelligence and response systems;
● Dynamic risk assessment frameworks for evolving ICPS and IoT architectures.

We eagerly anticipate receiving contributions that will illuminate the future pathways for constructing secure, private, and trustworthy ICPS and IoT systems enriched with the capabilities of LLMs.

15 Jun 2024