Research Article
A Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection
@INPROCEEDINGS{10.1007/978-3-030-19086-6_24, author={Tianshuang Li and Xiang Ji and Jingmei Li}, title={A Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={K-nearest neighbor algorithm Kmeans algorithm Trojan detection}, doi={10.1007/978-3-030-19086-6_24} }- Tianshuang Li
Xiang Ji
Jingmei Li
Year: 2019
A Speed-up K-Nearest Neighbor Classification Algorithm for Trojan Detection
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_24
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.
