基于xgboost的Android恶意软件检测

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00065

Jiong Wang, Boquan Li, Yuwei Zeng

{"title":"基于xgboost的Android恶意软件检测","authors":"Jiong Wang, Boquan Li, Yuwei Zeng","doi":"10.1109/CIS.2017.00065","DOIUrl":null,"url":null,"abstract":"Malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an Android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification. In the end, we verify the high efficacy and good accuracy of this model through the experiment, which achieves similar results to the SVM while spending less time.","PeriodicalId":304958,"journal":{"name":"2017 13th International Conference on Computational Intelligence and Security (CIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"XGBoost-Based Android Malware Detection\",\"authors\":\"Jiong Wang, Boquan Li, Yuwei Zeng\",\"doi\":\"10.1109/CIS.2017.00065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an Android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification. In the end, we verify the high efficacy and good accuracy of this model through the experiment, which achieves similar results to the SVM while spending less time.\",\"PeriodicalId\":304958,\"journal\":{\"name\":\"2017 13th International Conference on Computational Intelligence and Security (CIS)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Computational Intelligence and Security (CIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIS.2017.00065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Computational Intelligence and Security (CIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIS.2017.00065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 10

摘要

尽管智能手机系统不断升级，但恶意软件仍然是智能手机最大的安全威胁。本文介绍了一种基于XGBoost模型的Android恶意软件检测方法。我们随后讨论了特征选择对分类的影响。最后，我们通过实验验证了该模型的高效率和良好的准确性，在较少的时间内获得了与SVM相似的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

XGBoost-Based Android Malware Detection

Malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an Android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification. In the end, we verify the high efficacy and good accuracy of this model through the experiment, which achieves similar results to the SVM while spending less time.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 13th International Conference on Computational Intelligence and Security (CIS)

自引率

0.00%

发文量