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 |
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MDPI AG
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| Online-Zugang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3286316442 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-4990 | ||
| 024 | 7 | |a 10.3390/make7040115 |2 doi | |
| 035 | |a 3286316442 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Villafranca, Antonio |u Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain | |
| 245 | 1 | |a AI-Enabled IoT Intrusion Detection: Unified Conceptual Framework and Research Roadmap | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Literature Review | ||
| 520 | 3 | |a 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. | |
| 653 | |a Innovations | ||
| 653 | |a Accuracy | ||
| 653 | |a Performance measurement | ||
| 653 | |a Datasets | ||
| 653 | |a Internet of Things | ||
| 653 | |a Trends | ||
| 653 | |a Neural networks | ||
| 653 | |a Proposals | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Design | ||
| 653 | |a Blockchain | ||
| 653 | |a Literature reviews | ||
| 653 | |a Algorithms | ||
| 653 | |a Privacy | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Efficiency | ||
| 653 | |a Intrusion detection systems | ||
| 653 | |a Comparative analysis | ||
| 700 | 1 | |a Thant Kyaw Min |u Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain | |
| 700 | 1 | |a Tasic Igor |u Faculty of Economics and Business, UCAM Universidad Católica San Antonio de Murcia, 30107 Murcia, Spain | |
| 700 | 1 | |a Maria-Dolores, Cano |u Department of Information and Communication Technologies, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain | |
| 773 | 0 | |t Machine Learning and Knowledge Extraction |g vol. 7, no. 4 (2025), p. 115-153 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286316442/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286316442/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286316442/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |