欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对当前Android平台资源受限及恶意软件检测能力不足这一问题,以现有Android安装方式、触发方式和恶意负载方面的行为特征为识别基础,构建了基于ROM定制的Android软件行为动态监控框架,采用信息增益、卡方检验和Fisher Score的特征选择方法,评估了支持向量机（SVM）、决策树、k-邻近（KNN）和朴素贝叶斯（NB）分类器四类算法在Android恶意软件分类检测方面的有效性。通过对20 916个恶意样本及17 086个正常样本的行为日志的整体分类效果进行评估,结果显示,SVM算法在恶意软件判定上准确率可以达到93%以上,误报率低于2%,整体效果最优。可应用于在线云端分析环境和检测平台,满足海量样本处理需求。
英文摘要：
Concerning the constrained resources and low detection rate of Android, a software behavior dynamic monitoring framework based on ROM was constructed by considering behavior characteristics of Android in installation mode,trigger mode and malicious load,and the effectivenesses of Support Vector Machine（ SVM）,decision tree,k-Nearest Neighbor（ KNN） and Naive Bayesian（ NB） classifier were evaluated using information gain,chi square test and Fisher Score. The results of evaluation on overall classification of the behavior log of 20 916 malicious samples and 17 086 normal samples show that SVM has the best performance in the detection of malicious software,its accuracy rate can reach 93%,and the False Positive Rate（ FPR） is less than 2%. It can be applied to the online cloud analysis environment and detection platform,as well as meeting the needs of mass sample processing.
同期刊论文项目
同项目期刊论文
期刊信息
位置：