基于android的恶意软件规避技术和检测框架的调查与评估

Inf. Comput. Pub Date : 2023-06-30 DOI:10.3390/info14070374
Parvez Faruki, R. Bhan, V. Jain, Sajal Bhatia, Nour El Madhoun, Raj Pamula
{"title":"基于android的恶意软件规避技术和检测框架的调查与评估","authors":"Parvez Faruki, R. Bhan, V. Jain, Sajal Bhatia, Nour El Madhoun, Raj Pamula","doi":"10.3390/info14070374","DOIUrl":null,"url":null,"abstract":"Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks\",\"authors\":\"Parvez Faruki, R. Bhan, V. Jain, Sajal Bhatia, Nour El Madhoun, Raj Pamula\",\"doi\":\"10.3390/info14070374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.\",\"PeriodicalId\":13622,\"journal\":{\"name\":\"Inf. Comput.\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Inf. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/info14070374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Inf. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/info14070374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

Android平台安全是一个活跃的研究领域,恶意软件检测技术不断发展,以识别新的恶意软件,并提高对现有恶意软件的及时和准确检测。攻击者不断负责采用创新技术来有效地避免或延长恶意软件检测。过去的研究表明,恶意软件检测系统容易受到逃避攻击,攻击者可以成功绕过现有的安全防御,将恶意软件传递到目标系统而不被检测到。防逃逸系统的演化是一个开放的研究问题。本文介绍了基于android的恶意软件规避技术的详细分类和评估,这些技术用于规避恶意软件检测。该研究将这种规避技术分为两大类,多态性和变形,并分析了基于恶意软件独特特征的隐形恶意软件检测技术。此外,本文还对基于android的恶意软件的规避检测框架及其检测方法进行了定性和系统的比较。最后,调查讨论了开放式问题和移动恶意软件检测持续研究的潜在未来方向。
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
A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.
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
0.00%
发文量
0
期刊最新文献
Traceable Constant-Size Multi-authority Credentials Pspace-Completeness of the Temporal Logic of Sub-Intervals and Suffixes Employee Productivity Assessment Using Fuzzy Inference System Correction of Threshold Determination in Rapid-Guessing Behaviour Detection Combining Classifiers for Deep Learning Mask Face Recognition
×
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