| Autor: |
Umang Garg, Santosh Kumar, Aniket Mahanti |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Future Internet, Vol 16, Iss 6, p 212 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1999-5903 |
| DOI: |
10.3390/fi16060212 |
| Popis: |
The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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