Abstract
Aiming at the problem that the traditional K-nearest neighbor algorithm has a long classification time when predicting Trojan sample categories, this paper proposes a speed-up K-nearest neighbor classification algorithm CBBFKNN for Trojan detection. This method adopts the idea of rectangular partitioning to reduce the dimensionality of the sample data. Combining the simulated annealing algorithm and the Kmeans algorithm, the sample set is compressed and the BBF algorithm is used to quickly classify the sample. The experimental results show that, the CBBFKNN classification algorithm can effectively reduce the classification time while the precision loss is small in IRIS dataset. In terms of Trojan detection, the CBBFKNN classification algorithm can guarantee higher accuracy and lower misjudgment rate and lower missed detection rate in shorter detection time.
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This work was supported by National Key Research and Development Plan of China (No 2016YFB0801004).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, T., Ji, X., Li, J. (2019). A Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_24
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DOI: https://doi.org/10.1007/978-3-030-19086-6_24
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