中圖分類(lèi)號(hào)： TP306.2
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2022.04.005
引用格式： 田韻嵩，李中偉，譚凱，等. 汽車(chē)CAN總線入侵檢測(cè)算法性能模糊測(cè)試方法研究[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(4)：32-38.

Research on fuzzy test method of the detection ability of in-vehicle CAN bus intrusion detection algorithm

Abstract： The test results of the current vehicle CAN bus intrusion detection algorithm performance fuzzy test method are not highly reliable, due to the low test case coverage and poor pertinence. Aiming at this problem，an improved in-vehicle CAN bus intrusion detection algorithm performance fuzzy test method was proposed. According to whether the CAN bus protocol specification was known or not, fuzzy test cases were generated based on field weights or improved Wasserstein Generative Adversarial Network(WGAN-GP). The generated test cases were used to test the KNN algorithm and the AdaBoost algorithm. The test results showed that the detection performance of the AdaBoost algorithm was better than that of the KNN algorithm. The test verified that the test method proposed in this paper can obtain the test results with high reliability when used to test the performance of the intrusion detection algorithm, and achieved the purpose of providing a reference for the selection of the intrusion detection algorithm of the in-vehicle CAN bus.

Key words : intrusion detection algorithm；detecting ability test；Controller Area Network(CAN)；Generative Adversarial Network(GAN)； fuzzy test