Malware homology identification is useful for malware authorship attribution

attack scenario restoration

and so on.Current malware homology identification methods still rely on manual analysis

which is inefficient and time-consuming.In order to improve the effectiveness and efficiency

an automatic malware homology identification method is proposed.Based on 7-class calling behaviors

this method constructs a model of calling habits using data mining algorithms.Then it calculates the degree of homology based on Frequent Pattern Outlier Factor.Finally

it chooses the threshold values using k-means clustering algorithm to identify homology.The experimental evaluations on real-world malwares show our method achieves high accuracy (over 99%) and acceptable recall rate.