| Autor: |
Luying Wang, Lingyi Chen, Neal N. Xiong, Anfeng Liu, Tian Wang, Mianxiong Dong |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Digital Communications and Networks, Vol 10, Iss 1, Pp 168-181 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-8648 |
| DOI: |
10.1016/j.dcan.2023.06.007 |
| Popis: |
Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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