基于优化K-means的Android系统恶意软件检测的研究与设计

Research and Design of Malware Detection based on Optimized K-Means for Android System

由于开放性等特点,Android已成为目前应用最广泛的移动终端平台。然而,针对它的恶意软件层出不穷。为了检测这些安全隐患,人们提出了很多检测系统。但是,这些系统都存在一些缺陷,不但需要消耗较多资源,而且准确率不高。通过分析现有系统的不足,首先给出恶意软件检测系统的总体设计方案,其次在提取Android应用程序的特征参数后,重点设计和优化了特征聚类算法(k-means算法)。经仿真验证,设计的系统可以快速、有效地识别出恶意软件,具有重要的理论和应用价值。

Because of its openness and other characteristics,the Android system has become the most widely used mobile terminal platform.However,malware targeting it is also coming out one after the other.In order to detect these safety hazards,many detection systems have been proposed.But these systems have some drawbacks,which not only consume more resources,but also have low accuracy.By analyzing the deficiencies of the existing systems,the overall design of the malware detection system is given.Then,after extracting the feature parameters of the Android application,the feature clustering algorithm(k-means algorithm)is designed and optimized.The simulation proves that the designed system can identify malware quickly and effectively,which has important theoretical and application value.