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	Partitioning Based N-Gram Feature Selection for Malware Classification
	Hu, Weiwei ; Tan, Ying
	2016
关键词	Malware classification Feature selection Data partitioning Apache Spark
英文摘要	Byte level N-Gram is one of the most used feature extraction algorithms for malware classification because of its good performance and robustness. However, the N-Gram feature selection for a large dataset consumes huge time and space resources due to the large amount of different N-Grams. This paper proposes a partitioning based algorithm for large scale feature selection which efficiently resolves the original problem into in-memory solutions without heavy IO load. The partitioning process adopts an efficient implementation to convert the original interactional dataset to unrelated data partitions. Such data independence enables the effectiveness of the in-memory solutions and the parallelism on different partitions. The proposed algorithm was implemented on Apache Spark, and experimental results show that it is able to select features in a very short period of time which is nearly three times faster than the comparison MapReduce approach.; CPCI-S(ISTP); weiwei.hu@pku.edu.cn; ytan@pku.edu.cn; 187-195; 9714
语种	英语
出处	1st International Conference on Data Mining and Big Data (DMBD)
DOI标识	10.1007/978-3-319-40973-3_18
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/460183]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hu, Weiwei,Tan, Ying. Partitioning Based N-Gram Feature Selection for Malware Classification. 2016-01-01.

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