比较传统杀毒软件的Android恶意软件变体检测

Mahamat Hassan, I. Sogukpinar
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引用次数: 0

摘要

Android正逐渐成为恶意软件的目标。根据赛门铁克最近的威胁报告,从2016年到2017年,新发现的移动恶意软件变种数量增长了54%。恶意软件编写者使用混淆技术来创建恶意软件变体,以逃避某些工具检测或反病毒公司的检测。如果数据库不更新,反病毒软件很难检测到这些变体的签名。因此,探索预防、检测和反击网络攻击的新方法至关重要。在这些检测机制中,机器学习用于创建分类器,以确定应用程序是否危险。在研究中,我们主要关注Android APK中的Android恶意软件检测。我们分析了恶意软件编写者用来创建恶意软件变体的混淆技术。我们分析了Android APK的权限和API调用。我们比较了技术和传统的反病毒如何不容易检测恶意软件变体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Android Malware Variant Detection by Comparing Traditional Antivirus
Android is gradually becoming malware targeting it. According to the recent Symantec threat reports, the number of newly discovered mobile malware variants grew by 54% from 2016 to 2017. Malware writers used obfuscation techniques to create malware variants to evade detection by some tools detections or antivirus companies. it is difficult for antivirus to detect the signature of these variants if the database does not update. It is, therefore, essential to explore new ways to prevent, detect and counter cyberattacks. In these detection mechanisms, machine learning uses to create classifiers that determine whether an application is dangerous or not. In the research, we focus on Android malware detection in Android APK. We analyze obfuscation techniques used by malware writers to create malware variants. We analyze permission and API Calls from Android APK. We compare techniques and how it is not easy for traditional antivirus to detect malware variants.
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