Guest Editors
Prof. Ilsun You
Email: ilsunu@gmail.com
Affiliation: Department of Information Security, Cryptology, and Mathematics, Kookmin University, Seoul, South Korea
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Research Interests: Security for Mobile Networks; Authentication and Access Control; Formal Security Analysis; Insider Threats and Information Leakage Prevention; Digital Rights Management and Code Protection
Prof. Karl Andersson
Email: karl.andersson@ltu.se
Affiliation: Computer Science and Technology, the Royal Institute of Technology, Stockholm, Sweden
Homepage:
Research Interests: Cybersecurity, Blockchain Network, Deep Learning, Convolutional Neural Network, Machine Learning
Summary
As the integration of the Internet of Things (IoT) with Cloud-Edge-End architecture accelerates, it becomes increasingly pivotal in transforming various sectors, including smart cities, healthcare, industrial automation, and beyond. This convergence not only facilitates seamless connectivity and smart data exchange between devices but also introduces a new array of security challenges that must be addressed to ensure the reliability and safety of these systems. Current challenges in securing IoT within the Cloud-Edge-End architecture include, but are not limited to, the following: Distributed System Vulnerabilities, Real-time Data Processing Threats, Multi-layer Security Management, Privacy Concerns in Data Aggregation and Transmission, Anomalous Behavior Detection in Resource-constrained Environments, and Integration of Machine Learning (ML), especially Large Language Models (LLMs) for Enhanced Security. The complexity of these interconnected systems requires novel approaches and robust security measures to protect against evolving cyber threats and ensure the integrity of IoT networks. This Special Issue is dedicated to pioneering research addressing the multifaceted security concerns specific to IoT Intrusion Detection Systems (IDS) in the Cloud-Edge-End architecture. We aim to explore the security dimensions, starting from the foundational layers of hardware security and applied cryptography, to the sophisticated upper layers of secure communication protocols. Specifically, we are investigating the application of new artificial intelligence (AI) tools, such as LLMs, in IoT IDS to achieve enhanced intelligence, aligning with the advancements of the AI era. We invite researchers, academicians, and industry experts to contribute original research articles, reviews, and case studies that focus on innovative approaches, effective defense mechanisms, and insightful analyses aimed at enhancing the resilience of IoT systems within the Cloud-Edge-End paradigm. Contributions that present groundbreaking methodologies, effective defensive strategies, or detailed evaluations aimed at fortifying IoT systems against continuously evolving threats are particularly welcomed. Your research could lay the groundwork for the next generation of security solutions in this interconnected digital landscape.
This special issue aims to consolidate pioneering research focused on the development and enhancement of IoT Intrusion Detection Systems (IDS) within the Cloud-Edge-End architecture. The goal is to provide a comprehensive exploration of the security dimensions across all layers, from foundational hardware security and cryptographic techniques to advanced AI-driven detection methods. We are particularly interested in research that applies cutting-edge tools such as LLMs to create intelligent, adaptive, and scalable IDS solutions.
Topics of interest include, but are not limited to:
Architecture and Frameworks:
· Novel IDS frameworks for Cloud-Edge-End architecture
· Comparative studies of IDS architectures
· Integration of IDS with existing IoT infrastructure
Detection Techniques and Algorithms:
· Machine learning and AI-based intrusion detection methods
· Anomaly detection in heterogeneous IoT environments
· Signature-based vs. behavior-based detection techniques
Deployment and Scalability:
· Lightweight IDS solutions for edge devices
· Scalability of IDS in large-scale IoT deployments
· Real-time detection and response mechanisms
Performance Evaluation:
· Metrics and benchmarks for IDS performance in IoT
· Case studies and empirical evaluations of IDS
· Simulation and modeling of IDS in IoT scenarios
Security and Privacy:
· Privacy-preserving IDS techniques
· Secure communication protocols for IDS
· Threat modeling and risk assessment in IoT IDS
Applications and Case Studies:
· IDS in smart homes, smart cities, healthcare, and industrial IoT
· Case studies on successful IDS implementations
· Emerging trends and future directions in IoT security
Keywords
IoT Security, Intrusion Detection Systems (IDS), Cloud-Edge-End Architecture, Anomaly Detection, Machine Learning, Large Language Models (LLMs), Cybersecurity, Data Privacy, Distributed Systems, Real-time Detection, Smart Devices, Network Security, Threat Modeling, Scalability, Privacy-preserving, Techniques, Secure Communication Protocols
Published Papers
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