摘要
传统Android恶意行为识别方法无法解决恶意行为特征的动态波动性,识别出的恶意行为精度差,并且需要耗费大量的时间,因此提出基于RBF神经网络的Android恶意行为识别方法。该方法首先进行Android恶意行为的样本采集、行为特征提取和数据整合,使输出的结果可以被RBF神经网络识别,然后采用RBF神经网络局部逼近的特点提高学习速度,增强神经网络结果的输出质量,并采用K均值聚类算法得到所有特征集中各样本到该特征集中心距离的平方和,取该距离的最小值,通过最小二乘递推法计算隐含层节点到数据输出层节点的权值,完成RBF神经网络的训练,实现Android恶意行为的准确识别。实验结果说明所提方法可以提高对Android恶意行为识别的正确率和效率。
The traditional Android malicious behavior recognition method can′t solve the dynamic fluctuations of malicious behavior characteristics,has poor accuracy of malicious behavior recognition,and spends a large amount of time.Therefore,the Android malicious behavior recognition method based on RBF neural network is proposed.The items of sample acquisition of Android malicious behavior,behavior feature extraction and data integration are carried out to make that the output result can be identified by RBF neural network.The local approximation characteristic of RBF neural network is adopted to improve the output quality of neural network and learning speed.The K-means clustering algorithm is adopted to get the quadratic sum of distances from each sample in the feature set to the center of the feature set,so as to obtain the minimum distance.The least square recursive method is used to calculate the weight from the node of hidden layer to the node of data output layer,accomplish the training of RBF neural network,realize the accurate identification of Android malicious behavior.The experimental results show that the proposed method can improve the accuracy and efficiency of Android malicious behavior recognition.
作者
陈天伟
CHEN Tianwei(University of Electronic Science and Technology of China,Chengdu 610101,China;Urban Vocational College of Sichuan,Chengdu 610110,China)
出处
《现代电子技术》
北大核心
2018年第15期83-86,91,共5页
Modern Electronics Technique
基金
四川省教育厅重点科研项目(17ZA0236)~~
