Drift management in ML-based IoT device classification: A survey and evaluation
The fast-moving adaptation of the Internet of Things (IoT) and its devices has revolutionized the way we interact with the connecting things and perceive the world around us. Effective and efficient classification of these IoT devices is essential for network management, security, QoS and performanc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
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Indonesian Society for Knowledge and Human Development
2024
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| Online Access: | https://umpir.ump.edu.my/id/eprint/40500/ |
| _version_ | 1848827305186557952 |
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| author | Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Ali Khan, Arshad |
| author_facet | Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Ali Khan, Arshad |
| author_sort | Waseem, Quadri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The fast-moving adaptation of the Internet of Things (IoT) and its devices has revolutionized the way we interact with the connecting things and perceive the world around us. Effective and efficient classification of these IoT devices is essential for network management, security, QoS and performance optimization. However, the dynamic nature of IoT environments introduces a pervasive challenge for effective IoT device classification in the form of drift. Drift is characterized by shifts in device characteristics and behaviors over time, which poses a significant obstacle to accurately classifying IoT devices in various applications like smart homes, smart infrastructures, smart cities, etc. This survey explores the use of machine learning techniques to manage drift management in IoT device classification, encompassing drift prevention detection, drift adaptation, and drift mitigation. Additionally, we discuss the open issues and challenges of various Machine Learning (ML)-based models used to assess drift prevention, detection, adaptation techniques, and mitigation, along with future directions. By shedding light on the current landscape of drift management, this research survey aims to provide valuable insights regarding critical analysis for research gaps in drift management. |
| first_indexed | 2025-11-15T03:58:36Z |
| format | Article |
| id | ump-40500 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:36Z |
| publishDate | 2024 |
| publisher | Indonesian Society for Knowledge and Human Development |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-405002025-09-03T08:27:58Z https://umpir.ump.edu.my/id/eprint/40500/ Drift management in ML-based IoT device classification: A survey and evaluation Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Ali Khan, Arshad QA75 Electronic computers. Computer science The fast-moving adaptation of the Internet of Things (IoT) and its devices has revolutionized the way we interact with the connecting things and perceive the world around us. Effective and efficient classification of these IoT devices is essential for network management, security, QoS and performance optimization. However, the dynamic nature of IoT environments introduces a pervasive challenge for effective IoT device classification in the form of drift. Drift is characterized by shifts in device characteristics and behaviors over time, which poses a significant obstacle to accurately classifying IoT devices in various applications like smart homes, smart infrastructures, smart cities, etc. This survey explores the use of machine learning techniques to manage drift management in IoT device classification, encompassing drift prevention detection, drift adaptation, and drift mitigation. Additionally, we discuss the open issues and challenges of various Machine Learning (ML)-based models used to assess drift prevention, detection, adaptation techniques, and mitigation, along with future directions. By shedding light on the current landscape of drift management, this research survey aims to provide valuable insights regarding critical analysis for research gaps in drift management. Indonesian Society for Knowledge and Human Development 2024-06-25 Article PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/40500/1/Drift%20Management%20in%20ML-Based%20IoT%20Device%20Classification.pdf pdf en cc_by_sa_4 https://umpir.ump.edu.my/id/eprint/40500/7/Drift%20Management%20in%20ML-Based%20IoT%20Device%20Classification%20A%20Survey%20and%20Evaluation.pdf Waseem, Quadri and Wan Isni Sofiah, Wan Din and Azamuddin, Ab Rahman and Ali Khan, Arshad (2024) Drift management in ML-based IoT device classification: A survey and evaluation. International Journal on Advanced Science, Engineering and Information Technology, 15 (3). pp. 1-14. ISSN 2088-5334. (Published) https://doi.org/10.18517/ijaseit.15.3.13070 https://doi.org/10.18517/ijaseit.15.3.13070 https://doi.org/10.18517/ijaseit.15.3.13070 |
| spellingShingle | QA75 Electronic computers. Computer science Waseem, Quadri Wan Isni Sofiah, Wan Din Azamuddin, Ab Rahman Ali Khan, Arshad Drift management in ML-based IoT device classification: A survey and evaluation |
| title | Drift management in ML-based IoT device classification: A survey and evaluation |
| title_full | Drift management in ML-based IoT device classification: A survey and evaluation |
| title_fullStr | Drift management in ML-based IoT device classification: A survey and evaluation |
| title_full_unstemmed | Drift management in ML-based IoT device classification: A survey and evaluation |
| title_short | Drift management in ML-based IoT device classification: A survey and evaluation |
| title_sort | drift management in ml-based iot device classification: a survey and evaluation |
| topic | QA75 Electronic computers. Computer science |
| url | https://umpir.ump.edu.my/id/eprint/40500/ https://umpir.ump.edu.my/id/eprint/40500/ https://umpir.ump.edu.my/id/eprint/40500/ |