GSB: GNGS and SAG-BiGRU network for malware dynamic detection.
With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298809&type=printable |
| _version_ | 1797194867450839040 |
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| author | Zhanhui Hu Guangzhong Liu Xinyu Xiang Yanping Li Siqing Zhuang |
| author_facet | Zhanhui Hu Guangzhong Liu Xinyu Xiang Yanping Li Siqing Zhuang |
| author_sort | Zhanhui Hu |
| collection | DOAJ |
| description | With the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories' malware. In the dataset sample, the normal data samples account for the majority, while the attacks' malware accounts for the minority. However, the minority categories' attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks' malware to improve the detection rate of attacks' malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories' malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection. |
| first_indexed | 2024-04-24T06:03:06Z |
| format | Article |
| id | doaj.art-70bc418f6a5e493b9f1fbc1463e8605c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-24T06:03:06Z |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-70bc418f6a5e493b9f1fbc1463e8605c2024-04-23T05:31:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01194e029880910.1371/journal.pone.0298809GSB: GNGS and SAG-BiGRU network for malware dynamic detection.Zhanhui HuGuangzhong LiuXinyu XiangYanping LiSiqing ZhuangWith the rapid development of the Internet, the continuous increase of malware and its variants have brought greatly challenges for cyber security. Due to the imbalance of the data distribution, the research on malware detection focuses on the accuracy of the whole data sample, while ignoring the detection rate of the minority categories' malware. In the dataset sample, the normal data samples account for the majority, while the attacks' malware accounts for the minority. However, the minority categories' attacks will bring great losses to countries, enterprises, or individuals. For solving the problem, this study proposed the GNGS algorithm to construct a new balance dataset for the model algorithm to pay more attention to the feature learning of the minority attacks' malware to improve the detection rate of attacks' malware. The traditional malware detection method is highly dependent on professional knowledge and static analysis, so we used the Self-Attention with Gate mechanism (SAG) based on the Transformer to carry out feature extraction between the local and global features and filter irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. In the study, we used the Alibaba Cloud dataset for malware multi-classification. Compared the GSB deep learning network model with other current studies, the experimental results showed that the Gaussian noise generation strategy (GNGS) could solve the unbalanced distribution of minority categories' malware and the SAG-BiGRU algorithm obtained the accuracy rate of 88.7% on the eight-classification, which has better performance than other existing algorithms, and the GSB model also has a good effect on the NSL-KDD dataset, which showed the GSB model is effective for other network intrusion detection.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298809&type=printable |
| spellingShingle | Zhanhui Hu Guangzhong Liu Xinyu Xiang Yanping Li Siqing Zhuang GSB: GNGS and SAG-BiGRU network for malware dynamic detection. PLoS ONE |
| title | GSB: GNGS and SAG-BiGRU network for malware dynamic detection. |
| title_full | GSB: GNGS and SAG-BiGRU network for malware dynamic detection. |
| title_fullStr | GSB: GNGS and SAG-BiGRU network for malware dynamic detection. |
| title_full_unstemmed | GSB: GNGS and SAG-BiGRU network for malware dynamic detection. |
| title_short | GSB: GNGS and SAG-BiGRU network for malware dynamic detection. |
| title_sort | gsb gngs and sag bigru network for malware dynamic detection |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0298809&type=printable |
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