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Concept drift detection and adaptation method for IoT security framework
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AI
DOI：10.23919/JCC.fa.2022-0379.202512.png)
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Abstract
En 中文
With the gradual penetration of the internet of things (IoT) into all areas of life, the scale of IoT devices shows an explosive growth trend. The era of internet of everything is coming, and the important position of IoT security is becoming increasingly prominent. Due to the large number types of IoT devices, there may be different security vulnerabilities, and unknown attack forms and virus samples are appear. In other words, large number of IoT devices, large data volumes, and various attack forms pose a big challenge of malicious traffic identification. To solve these problems, this paper proposes a concept drift detection and adaptation (CDDA) method for IoT security framework. The AI model performance is evaluated by verifying the effectiveness of IoT traffic for data drift detection, so as to select the best AI model. The experimental test are given to confirm that the feasibility of the framework and the adaptive method in practice, and the effect on the performance of IoT traffic identification is also verified.
Keywords:
concept drift detection and adaptive (CDDA) method
IoT security
malicious traffic identification
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