摘要
计算机反病毒厂商每天接收成千上万的病毒样本,如何快速有效地将这些海量样本家族化是一个亟待解决的问题。提出了一种可伸缩性的聚类方法,面对输入海量的病毒样本向量化特征集,使用局部敏感哈希索引技术进行初次快速聚类,使用扩展K均值算法进行二次细致聚类。实验表明该聚类方法在有限牺牲准确度的情况下,大为提高了病毒聚类的时间效率。
Anti-malware companies receive thousands of malware samples every day, so it becomes more and more pressing to handle these samples timely and effectively. A scalable clustering approach is proposed to group these massive malware samples. LSH algorithm is used to cluster samples rapidly. Extended K-means algorithm is employed to perform accurately clustering. Experimental results show that this approach can improve malware clustering efficiency observably at the cost of little accuracy.
出处
《计算机工程与应用》
CSCD
2014年第18期118-121,共4页
Computer Engineering and Applications
关键词
病毒家族
可伸缩性聚类
局部敏感哈希
扩展K均值
malware family
scalable clustering
Locality Sensitive Hash（LSH）algorithm
extended K-means
