HEMD: a highly efficient random forest-based malware detection framework for Android

Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.

     

基于JSON的离线数据同步策略及应用

针对智能移动应用的特殊性及其在离线情况下的数据同步问题,提出了一种数据同步方案,使用JSON技术设计数据交换协议,移动端离线数据存放在SQLite数据库中、使用基于时间戳的冲突检测算法提高同步的准确性,并采用增量同步方式保证同步的效率和准确性. 将该策略应用在智慧安防系统中,结果表明,基于JSON离线数据同步效率相比传统基于XML的方案提高约8%.