用机器学习检测安卓恶意APK，识别诈骗、木马等威胁
Machine Learning-based Android Intrusion Detection System
- 基于多参数特征分析APK行为，分类判断是否恶意
- 通过机器学习模型识别新安装应用的恶意特征
- 适合安全研究人员和移动设备防护开发者参考
安卓操作系统正广泛部署于各类智能设备，随之而来的入侵事件呈指数级增长。恶意数据流导致设备面临钓鱼、间谍软件、短信诈骗、僵尸网络及银行木马等多种攻击。本文利用机器学习分类算法对安卓APK文件进行安全检测，根据多种参数将每个APK数据流标记为恶意或非恶意。随后采用机器学习分类技术判断新安装应用的签名是否属于恶意范畴。若判定为恶意，则可采取相应防护措施，从而提升安卓系统对非法活动的抵御能力。
原文摘要 · Abstract (English)
The android operating system is being installed in most of the smart devices. The introduction of intrusions in such operating systems is rising at a tremendous rate. With the introduction of such malicious data streams, the smart devices are being subjected to various attacks like Phishing, Spyware, SMS Fraud, Bots and Banking-Trojans and many such. The application of machine learning classification algorithms for the security of android APK files is used in this paper. Each apk data stream was marked to be either malicious or non malicious on the basis of different parameters. The machine learning classification techniques are then used to classify whether the newly installed applications' signature falls within the malicious or non-malicious domain. If it falls within the malicious category, appropriate action can be taken, and the Android operating system can be shielded against illegal activities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。