基于主动学习的计算机病毒检测方法研究

针对传统病毒检测方法存在的更新速度慢、对未知病毒检测能力不足等问题,该文对主动学习理论在计算机病毒检测方面的应用进行了研究,提出了一种基于支持向量机主动学习的计算机病毒检测模型结构。此外,为了改进病毒检测的精度问题及主动学习过程的效率,利用相关n-gram方法实现了对样本文件的特征提取,并结合信任度测量理论实现了基于非确定抽样的询问功能。实验表明,该模型针对未知病毒具有较高的检测精度,并且能够极大地缩减训练时间及对训练数据的数量要求,提高系统的学习效率。

Research on Computer Viruses Detection Approaches Based on Active Learning

Traditional computer viruses detection approaches update slowly and have poor ability in detecting unknown viruses.In this paper,the application of the active learning theory in computer viruses detection is studied,and a computer viruses detection model based on active learning of the support vector machine is proposed.Moreover,to improve the precision of the virus detection and the efficiency of the active learning process,feature extraction of the sampling files are realized by using the method of relevant n-gram,and combined with the trust measurement theory,query function based on the uncertainty based sampling is also realized.Experiments＇ results show that the model has very good detection precision against unknown computer viruses and can greatly shorten the training time and reduce the requirements of the training data and improve the learning efficiency of the system.