Effective of Obfuscated Android Malware Detection using Static Analysis

T. Mantoro, Muhammad Elky Fahriza, Muhammad Agni Catur Bhakti
{"title":"Effective of Obfuscated Android Malware Detection using Static Analysis","authors":"T. Mantoro, Muhammad Elky Fahriza, Muhammad Agni Catur Bhakti","doi":"10.1109/ICCED56140.2022.10010587","DOIUrl":null,"url":null,"abstract":"The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.","PeriodicalId":200030,"journal":{"name":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCED56140.2022.10010587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.
有效的模糊Android恶意软件检测使用静态分析
针对Android操作系统恶意软件攻击的有效安全系统改进需要更新和完善。有效的恶意软件检测提高了数据的安全性和对用户的高度保护。恶意软件或恶意软件通常会找到绕过安全程序的方法,即使用户不知道应用程序是否可以作为恶意软件。通过从数据集中收集静态分析数据,评估了模糊android恶意软件检测的有效性。实验利用恶意软件的哈希值评估恶意软件数据集的风险级别,并记录恶意软件的行为。从互联网数据集中获得了一组散列SHA256恶意软件样本,并将使用静态分析来记录恶意软件行为并评估恶意软件的风险级别。结果表明,由于恶意软件应用程序内部存在多重犯罪,大多数算法提供的总分相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信