Android malware detection using multivariate time-series technique
Android malware detection using multivariate time-series technique
复制标题
使用多元时间序列技术的 Android 恶意软件检测
DOI：
10.1109/apnoms.2015.7275426
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Mi
中科院分区：
文献类型：
--
作者：
Ki;Mi
最近，智能设备的使用与它们的性能改进并行地继续传播。智能设备的激增导致了各种服务的出现，例如信使、SNS和智能银行，并带来了使用服务的便利。然而，另一方面也面临着被称为安全漏洞的威胁。此类威胁造成的危害主要有个人信息泄露、不合理收费、root权限获取等，此外，据称Android被认为是智能设备操作系统中最易受攻击的操作系统，其恶意代码的危害最大。因此，本文提出了一种基于多变量时间序列分析的Android设备恶意代码检测方法。将多种资源信息整合到一个资源中组织数据，采用时间序列模型中的自回归滑动平均模型进行建模。模型化的数据与真实的数据相匹配以检测恶意代码。实验结果表明了该方法的有效性和优越性。
Recently, use of smart devices has continued to spread in parallel with their performance improvement. The proliferation of smart devices has led to an emergence of various services such as messengers, SNS and smart banking, and brought convenience in using the services. However, the threat called security vulnerabilities is being faced on the other side. The damages suffered from such a threat are personal information leakage, unreasonable charging, root permission acquisition and so on. In addition, it is said that Android, which is considered as the most vulnerable operating system among the smart devices' operating systems, has the greatest damage of malware codes. Accordingly, this paper proposes a technique to detect malicious codes based on Android devices by using the multivariate time-series analysis. A variety of resource information is integrated into a resource to organize data, and an autoregressive moving average model of the time-series models is used to carry out the modeling. The modeled data is matched with real data to detect malicious codes. The proposed method's validity and excellence is suggested through this experimental result.