基于系统调用的手机APP能耗漏洞检测

Detection of phone’s application energy bug based on system call

针对传统研究中APP能耗漏洞模型存在尾部能耗与应用对象限制的问题,构建基于系统调用的能耗漏洞模型。首先使用集合交叉递归特征消除法选择影响每个类别APP能耗的重要系统调用作为特征,提高特征细化粒度。然后为每个类别APP构建多个回归模型,通过比较不同模型的平均绝对误差与决定系数,选择线性核支持向量机回归作为分类APP的能耗模型。最后基于测试集比较集合交叉递归特征消除法与交叉递归特征消除法所构模型的平均绝对误差,结果表明集合交叉递归特征消除法所构模型精度最多提高4.4%,同时基于测试集比较分类模型与未分类模型的平均绝对误差,结果表明分类模型精度最多提高6.7%,并且分类模型能准确检测出APP历史版本的能耗漏洞。

Aiming at the problem of tail energy consumption and application object limitation in APP energy bug model in traditional research,a system call based energy bug model is constructed.First,mix-cross-recursive feature elimination method is used to select important system calls that affect the energy consumption of each category of APP as features to improve the feature refinement granularity.Then,multiple regression models are constructed for each category of APP.By comparing the average absolute error and coefficient of determination of different models,linear kernel support vector machine regression is selected as the energy bug model of the classified APP.Finally,based on the test set,the average absolute error of the model constructed by the mix cross recursive feature elimination method and the cross recursive feature elimination method is compared.The result shows that the accuracy of the model constructed by the mix recursive feature elimination method is increased by up to 4.4%.At the same time,the classification model is compared with the classification model based on the test set.The average absolute error of the unclassified model shows that the accuracy of the classification model is increased by up to 6.7%,and the classification model can accurately detect the energy bug of the historical version of the APP.