| Title | The Application of 1D-CNN in Microsoft Malware Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Huo, Da, Li, Xiaoyong, Li, Linghui, Gao, Yali, Li, Ximing, Yuan, Jie |
| Conference Name | 2022 7th International Conference on Big Data Analytics (ICBDA) |
| Date Published | mar |
| Keywords | 1D-CNN, composability, Databases, Deep Learning, feature engineering, feature extraction, LightGBM, Malware, malware detection, Metrics, Network security, Neural networks, pubcrawl, Resiliency, Semantics, Windows Operating System Security |
| Abstract | In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%. |
| DOI | 10.1109/ICBDA55095.2022.9760349 |
| Citation Key | huo_application_2022 |
The Application of 1D-CNN in Microsoft Malware Detection