metal learning for malware classification

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable whenencountering various types of attacks. Every day, new malware threatens the security of users’ devices and private data. Many methodshave been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methodsstill suffer from unsatisfactory performance due to two challenges随着智能手机的普及，安卓（Android）已成为应用最为广泛的移动操作系统。然而，其在面对各类攻击时往往存在安全脆弱性。每日均有新型恶意软件对用户设备安全及私有数据隐私构成威胁。目前已有诸多研究提出基于静态分析与动态分析的恶意应用分类方案，但受限于两项核心挑战，现有方法的分类性能仍未达到理想水平。