作者
Fatemeh Hosseini, Mitra Mirzarezaee, Arash Sharifi
发表日期
2019/9/10
期刊
Signal and Data Processing
卷号
16
期号
2
页码范围
137-146
出版商
Signal and Data Processing
简介
In this paper, a novel method based on the graph is proposed to classify the sequence of variable length as feature extraction. The proposed method overcomes the problems of the traditional graph with variable length of data, without fixing length of sequences, by determining the most frequent instructions and insertion the rest of instructions on the set of “other”, save speed and memory. According to features and the similarities of them, a score is given to each sample and that is used for classification. To improve the results, the method is not used alone, but in the two approaches, this method is combined with other existing Technique to get better results. In the first approach, which can be considered as a feature extraction, extracted features from scoring techniques (Hidden Markov Model, simple substitution distance and similarity graph) on op-code sequences, hexadecimal sequences and system calls are …
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