| Title: | OS4-4 MALWARE CLASSIFICATION USING DEEP LEARNING |
|---|---|
| Publication: | ICAROB2020 |
| Volume: | 25 |
| Pages: | 126-129 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2020.OS4-4 |
| Author(s): | Cheng-Hsiang Lo, Ta-Che Liu, I-Hsien Liu, Jung-Shian Li, Chuan-Gang Liu, Chu-Fen Li |
| Publication Date: | January 13, 2020 |
| Keywords: | NIDS, Dynamic analysis, Deep Learning |
| Abstract: | We'll display two different kinds of experiments, which are NIDS-based and Dynamic-based analysis shows how artificial intelligence (AI) helps us detecting and classify malware. On the NIDS-based intrusion detection, we use CICIDS2017 as a research dataset, embedding high dimensional features and find out redundant features in the raw dataset by Random Forest algorithm, reach 99.93% accuracy and 0.3% of the false alert rate. We extract the function calls in malware data by the method proposed in this paper to generate text data. The algorithm n-gram and TF-IDF are used to process text data, converts them into numeric features, and by another feature selection methods, we reduce the training time, achieve 87.08% accuracy, and save 87.97% training time in Dynamic-based analysis. |
| PDF File: | https://alife-robotics.co.jp/members2020/icarob/data/html/data/OS/OS4/OS4-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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