Malware Classification System Based on Machine Learning
| Title | Malware Classification System Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wei, Qu, Xiao, Shi, Dongbao, Li |
| Conference Name | 2019 Chinese Control And Decision Conference (CCDC) |
| Date Published | June 2019 |
| Publisher | IEEE |
| Keywords | Classification system, Entropy, feature extraction, feature selection, high-efficiency automatic classification system, Human Behavior, invasive software, learning (artificial intelligence), Libraries, machine learning, Malware, malware classification, malware classification system, Metrics, multifeature selection fusion, pattern classification, privacy, pubcrawl, resilience, Resiliency, static analysis |
| Abstract | The main challenge for malware researchers is the large amount of data and files that need to be evaluated for potential threats. Researchers analyze a large number of new malware daily and classify them in order to extract common features. Therefore, a system that can ensure and improve the efficiency and accuracy of the classification is of great significance for the study of malware characteristics. A high-performance, high-efficiency automatic classification system based on multi-feature selection fusion of machine learning is proposed in this paper. Its performance and efficiency, according to our experiments, have been greatly improved compared to single-featured systems. |
| URL | https://ieeexplore.ieee.org/document/8832802/ |
| DOI | 10.1109/CCDC.2019.8832802 |
| Citation Key | wei_malware_2019 |
- malware
- static analysis
- Resiliency
- resilience
- pubcrawl
- privacy
- pattern classification
- multifeature selection fusion
- Metrics
- malware classification system
- malware classification
- Classification system
- machine learning
- Libraries
- learning (artificial intelligence)
- invasive software
- Human behavior
- high-efficiency automatic classification system
- Feature Selection
- feature extraction
- Entropy