The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) into AIoT systems promises autonomous, intelligent operations. However, this fusion introduces unique security challenges stemming from heterogeneous architectures, resource-constrained devices, tightly coupled AI-IoT software stacks, and heightened autonomy. Ensuring the security of the underlying software and hardware components is therefore a fundamental prerequisite for the reliable deployment of AIoT.

While traditional cybersecurity has evolved significantly, the distinct nature of the AIoT environment complicates the direct application of existing solutions. This collection focuses on five pivotal and interconnected areas that demand novel research: System and Software Protection, Vulnerability/Bug Detection, Malware Detection, Software Testing, and Privacy for AIoT Systems. Each area presents acute technical questions. For instance: How can we develop practical, lightweight runtime integrity protection for diverse edge devices? How do we detect sophisticated malware designed to exploit both AI model logic and IoT protocol weaknesses? What testing frameworks can validate the security of autonomous AIoT decision-making processes? How can privacy be preserved in pervasive sensing and distributed AI inference?

This collection aims to compile high-quality research contributions focused on these concrete technical challenges. We seek original submissions that present innovative theories, methodologies, tools, or in-depth case studies specifically designed for the AIoT paradigm. By advancing knowledge in these core areas, this collection will contribute to building a robust security and privacy foundation for the next generation of intelligent connected systems. Potential topics include but are not limited to:

• System and software protection for AIoT (e.g., lightweight runtime integrity, secure boot, trusted execution in constrained devices)
• Vulnerability and bug detection in AIoT stacks (static and dynamic analysis, fuzzing, automated vulnerability discovery)
• Malware detection and attribution targeting AIoT (model-targeted attacks, protocol-level exploitation, hybrid malware)
• Security-focused software testing for autonomous AIoT (testing of decision-making, simulation-based evaluation, continuous validation)
• Privacy-preserving methods for AIoT (federated learning, secure aggregation, differential privacy, privacy-aware sensing)

This Collection supports and amplifies research related to SDG 9.

Keywords: AIoT systems; System and Software Security; Information Security; Privacy; IoT Protocols; Authentication; Vulnerability Detection

The Internet of Things (IoT) has revolutionised modern society by connecting billions of devices across various sectors, including smart homes, healthcare, transportation, and industrial systems. However, this connectivity introduces critical security and privacy challenges. This Topical Collection focuses on three transformative technologies—blockchain, differential privacy, and federated intelligence—that address these challenges.

Blockchain provides decentralized, tamper-proof infrastructures for IoT data integrity and device authentication. Differential privacy offers mathematically rigorous privacy guarantees for IoT data analytics while preserving utility. Federated intelligence enables collaborative machine learning across IoT devices without requiring centralization of sensitive data.

This collection aims to showcase cutting-edge research that integrates these technologies, addresses implementation challenges such as energy efficiency and scalability, and explores real-world applications in smart cities, Industrial IoT, healthcare, and vehicular networks. It will serve as a comprehensive resource for researchers and practitioners developing secure, privacy-preserving IoT ecosystems.

This Collection supports and amplifies research related to SDG3, SDG 9 and SDG11.

Keywords: Blockchain for IoT; Differential Privacy; Federated Learning; IoT Security; Privacy-Preserving IoT; Distributed Ledger Technology; Edge Intelligence; Secure Smart Systems; Federated Intelligence; IoT Data Privacy