Classifying Malware Using Convolutional Gated Neural Network
| Title | Classifying Malware Using Convolutional Gated Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Kim, C. H., Kabanga, E. K., Kang, S. |
| Conference Name | 2018 20th International Conference on Advanced Communication Technology (ICACT) |
| Date Published | Feb. 2018 |
| Publisher | IEEE |
| ISBN Number | 979-11-88428-01-4 |
| Keywords | CNN, convolutional gated recurrent neural network model, convolutional neural networks, Deep Neural Network, feedforward neural nets, Gated Recurrent Unit, Human Behavior, information technology society, invasive software, Logic gates, machine learning, malicious software, Malware, malware classification, malware detection, Metrics, microsoft malware classification challenge, Neural Network, pattern classification, privacy, pubcrawl, recurrent neural nets, Recurrent neural networks, resilience, Resiliency, Task Analysis |
| Abstract | Malware or Malicious Software, are an important threat to information technology society. Deep Neural Network has been recently achieving a great performance for the tasks of malware detection and classification. In this paper, we propose a convolutional gated recurrent neural network model that is capable of classifying malware to their respective families. The model is applied to a set of malware divided into 9 different families and that have been proposed during the Microsoft Malware Classification Challenge in 2015. The model shows an accuracy of 92.6% on the available dataset. |
| URL | https://ieeexplore.ieee.org/document/8323640 |
| DOI | 10.23919/ICACT.2018.8323640 |
| Citation Key | kim_classifying_2018 |
- malware classification
- Task Analysis
- Resiliency
- resilience
- Recurrent neural networks
- recurrent neural nets
- pubcrawl
- privacy
- pattern classification
- neural network
- microsoft malware classification challenge
- Metrics
- malware detection
- CNN
- malware
- malicious software
- machine learning
- Logic gates
- invasive software
- information technology society
- Human behavior
- Gated Recurrent Unit
- feedforward neural nets
- Deep Neural Network
- convolutional neural networks
- convolutional gated recurrent neural network model