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About Deep Learning For Classification of Malware System Call Sequences
This document proposes using deep learning neural networks to classify malware based on system call sequences. Specifically, it combines convolutional and recurrent neural network layers to model system call n-grams and sequences to extract hierarchical features for improved malware classification. Evaluation of the approach achieved average precision of 85.6% and recall of 89.4% for malware classification, outperforming previous methods. The document introduces the problem of large-scale malware detection, describes system calls as important behavioral data, reviews previous machine learning approaches, and presents the novel deep learning method of combining convolutional and recurrent neural network layers to better model system call sequences for classification.
Detailed Information
| Author: | ['Alina Ioana Moldovan'] |
|---|---|
| Publication Year: | 2017 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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