Buchumschlag

AI-Enabled IoT Intrusion Detection: Unified Conceptual Framework and Research Roadmap

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Veröffentlicht in:Machine Learning and Knowledge Extraction vol. 7, no. 4 (2025), p. 115-153
1. Verfasser: Villafranca, Antonio
Weitere Verfasser: Thant Kyaw Min, Tasic Igor, Maria-Dolores, Cano
Veröffentlicht:
MDPI AG
Schlagworte:
Innovations
Accuracy
Performance measurement
Datasets
Internet of Things
Trends
Neural networks
Proposals
Cybersecurity
Design
Blockchain
Literature reviews
Algorithms
Privacy
Artificial intelligence
Efficiency
Intrusion detection systems
Comparative analysis
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Abstract:The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems.
ISSN:2504-4990
DOI:10.3390/make7040115
Quelle:Advanced Technologies & Aerospace Database