DeepMDFC:一种基于深度学习的android恶意软件检测和分类方法

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Security and Privacy Pub Date : 2023-10-16 DOI:10.1002/spy2.347
Sandeep Sharma, Prachi Ahlawat, Kavita Khanna
{"title":"DeepMDFC:一种基于深度学习的android恶意软件检测和分类方法","authors":"Sandeep Sharma, Prachi Ahlawat, Kavita Khanna","doi":"10.1002/spy2.347","DOIUrl":null,"url":null,"abstract":"Abstract Unprecedented growth and prevalent adoption of the Android Operating System (OS) have triggered a substantial transformation, not only within the smartphone industry but across various categories of intelligent devices. These intelligent devices store a wealth of sensitive data, making them enticing targets for malicious individuals who create harmful Android applications to steal this data for malicious purposes. While numerous Android malware detection methods have been proposed, the exponential growth in sophisticated and malicious Android apps presents an unprecedented challenge to existing detection techniques. Some of the researchers have attempted to classify malicious Android applications into families through static analysis of applications but most of them are evaluated on applications of previous API levels. This paper introduces a novel dataset compromising of 2019 to 2021 applications and proposes a Deep Learning based Malware Detection and Family Classification method (DeepMDFC) to detect and classify emerging malicious Android applications through static analysis and deep Artificial Neural Networks. Experimental findings indicate that DeepMDFC surpasses standard machine learning algorithms, achieving accuracy rates of 99.3% and 96.7% for Android malware detection and classification, respectively, with a limited size feature set. The performance of DeepMDFC is also assessed using the benchmark dataset (DREBIN) and results showed that DeepMDFC surpasses these methods in terms of performance. Furthermore, it leverages the proposed dataset to construct a prediction model that adeptly identifies malicious Android applications from both the years 2022 and 2023. This process the potency and resilience of DeepMDFC against emerging Android applications.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":"36 1","pages":"0"},"PeriodicalIF":1.5000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"<scp>DeepMDFC</scp>: A deep learning based android malware detection and family classification method\",\"authors\":\"Sandeep Sharma, Prachi Ahlawat, Kavita Khanna\",\"doi\":\"10.1002/spy2.347\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Unprecedented growth and prevalent adoption of the Android Operating System (OS) have triggered a substantial transformation, not only within the smartphone industry but across various categories of intelligent devices. These intelligent devices store a wealth of sensitive data, making them enticing targets for malicious individuals who create harmful Android applications to steal this data for malicious purposes. While numerous Android malware detection methods have been proposed, the exponential growth in sophisticated and malicious Android apps presents an unprecedented challenge to existing detection techniques. Some of the researchers have attempted to classify malicious Android applications into families through static analysis of applications but most of them are evaluated on applications of previous API levels. This paper introduces a novel dataset compromising of 2019 to 2021 applications and proposes a Deep Learning based Malware Detection and Family Classification method (DeepMDFC) to detect and classify emerging malicious Android applications through static analysis and deep Artificial Neural Networks. Experimental findings indicate that DeepMDFC surpasses standard machine learning algorithms, achieving accuracy rates of 99.3% and 96.7% for Android malware detection and classification, respectively, with a limited size feature set. The performance of DeepMDFC is also assessed using the benchmark dataset (DREBIN) and results showed that DeepMDFC surpasses these methods in terms of performance. Furthermore, it leverages the proposed dataset to construct a prediction model that adeptly identifies malicious Android applications from both the years 2022 and 2023. This process the potency and resilience of DeepMDFC against emerging Android applications.\",\"PeriodicalId\":29939,\"journal\":{\"name\":\"Security and Privacy\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Security and Privacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/spy2.347\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Security and Privacy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/spy2.347","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

Android操作系统(OS)的空前增长和普遍采用引发了一场实质性的变革,不仅在智能手机行业,而且在各种智能设备领域。这些智能设备存储了大量的敏感数据,使它们成为恶意个人的诱人目标,恶意个人创建有害的Android应用程序来窃取这些数据以达到恶意目的。虽然已经提出了许多Android恶意软件检测方法,但复杂和恶意Android应用程序的指数增长对现有检测技术提出了前所未有的挑战。一些研究人员试图通过对应用程序的静态分析来对恶意Android应用程序进行分类,但大多数恶意Android应用程序都是在以前的API级别上进行评估的。本文介绍了一种新的2019年到2021年应用的数据集,并提出了一种基于深度学习的恶意软件检测和家族分类方法(DeepMDFC),通过静态分析和深度人工神经网络对新兴的恶意Android应用进行检测和分类。实验结果表明,DeepMDFC超越了标准的机器学习算法,在有限大小的特征集下,对Android恶意软件的检测和分类准确率分别达到99.3%和96.7%。DeepMDFC的性能也使用基准数据集(DREBIN)进行了评估,结果表明,DeepMDFC在性能方面优于这些方法。此外,它利用提出的数据集构建一个预测模型,熟练地识别2022年和2023年的恶意Android应用程序。这一过程证明了DeepMDFC对新兴Android应用程序的效力和弹性。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
DeepMDFC: A deep learning based android malware detection and family classification method
Abstract Unprecedented growth and prevalent adoption of the Android Operating System (OS) have triggered a substantial transformation, not only within the smartphone industry but across various categories of intelligent devices. These intelligent devices store a wealth of sensitive data, making them enticing targets for malicious individuals who create harmful Android applications to steal this data for malicious purposes. While numerous Android malware detection methods have been proposed, the exponential growth in sophisticated and malicious Android apps presents an unprecedented challenge to existing detection techniques. Some of the researchers have attempted to classify malicious Android applications into families through static analysis of applications but most of them are evaluated on applications of previous API levels. This paper introduces a novel dataset compromising of 2019 to 2021 applications and proposes a Deep Learning based Malware Detection and Family Classification method (DeepMDFC) to detect and classify emerging malicious Android applications through static analysis and deep Artificial Neural Networks. Experimental findings indicate that DeepMDFC surpasses standard machine learning algorithms, achieving accuracy rates of 99.3% and 96.7% for Android malware detection and classification, respectively, with a limited size feature set. The performance of DeepMDFC is also assessed using the benchmark dataset (DREBIN) and results showed that DeepMDFC surpasses these methods in terms of performance. Furthermore, it leverages the proposed dataset to construct a prediction model that adeptly identifies malicious Android applications from both the years 2022 and 2023. This process the potency and resilience of DeepMDFC against emerging Android applications.
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
自引率
5.30%
发文量
80
期刊最新文献
Physically secure and privacy‐preserving blockchain enabled authentication scheme for internet of drones A new authentication scheme for dynamic charging system of electric vehicles in fog environment Enhancing android application security: A novel approach using DroidXGB for malware detection based on permission analysis Designing access control security protocol for Industry 4.0 using Blockchain‐as‐a‐Service An efficient lightweight authentication scheme for dew‐assisted IoT networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1