基于卡方检验的Android恶意应用检测方法

An Android Malware Detection Method Based on Chi-Squared Test

移动终端爆发式增长造成了恶意应用的大量出现,给用户的隐私安全和财产安全带来了巨大的危害.为提高Android应用恶意性检测的准确性,本文将卡方检验与基尼不纯度增量相结合获取更有价值的特征属性;并改进朴素贝叶斯算法提高Android应用恶意性判断的准确性.实验结果表明:新的特征处理方法能够有效提高检测性能;同时,改进后的朴素贝叶斯算法相比原始算法而言准确率有较大的提升.

The explosive growth of mobile terminals has produced endless malicious applications,bring on great harm to the security of users’ privacy and property.To solve this problem,a method based on chi-squared test and Gini impurity increment was proposed for more valuable features extraction and the Naive Bayes algorithm improvement,so as to improve the estimation accuracy of Android malevolence applications.Test shows that the new features processing method can improve the classification performance of algorithms.At the same time,the improved Naive Bayes algorithm can achieve higher accuracy than before.