Qingjun Yuan;Weina Niu;Yongjuan Wang;Gaopeng Gou;Bin Lu
{"title":"基于跨模态一致性的噪声标签恶意流量检测","authors":"Qingjun Yuan;Weina Niu;Yongjuan Wang;Gaopeng Gou;Bin Lu","doi":"10.1109/LNET.2023.3349301","DOIUrl":null,"url":null,"abstract":"To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"148-151"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency\",\"authors\":\"Qingjun Yuan;Weina Niu;Yongjuan Wang;Gaopeng Gou;Bin Lu\",\"doi\":\"10.1109/LNET.2023.3349301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives.\",\"PeriodicalId\":100628,\"journal\":{\"name\":\"IEEE Networking Letters\",\"volume\":\"6 2\",\"pages\":\"148-151\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Networking Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10379482/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Networking Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10379482/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Malicious Traffic Detection With Noise Labels Based on Cross-Modal Consistency
To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives.