Using Generative Adversarial Networks for Data Augmentation in Android Malware Detection
作者： Yi-Ming Chen , Guo-Chung Chen , Chun-Hsien Yang
DOI: 10.1109/DSC49826.2021.9346277
关键词: Field (computer science) 、 Deep learning 、 Computer science 、 Artificial intelligence 、 Classifier (linguistics) 、 Android (operating system) 、 Image (mathematics) 、 Mobile malware 、 Small number 、 Sample (statistics) 、 Machine learning
摘要: In the field of mobile malware detection, the problem of sample imbalance often occurs in the dataset, making the classifier unable to learn features through sufficient data during the training process. This research used the generative adversarial networks (GAN). In this paper, features of malwares are transformed into image expressions, and data is generated from a small number of malicious families to balance and expand the original dataset. We also compare other data augmentation techniques to explore whether they are beneficial to …
sci-hub.st 参考文章(17)
L. Nataraj, S. Karthikeyan, G. Jacob, B. S. Manjunath, Malware images: visualization and automatic classification visualization for computer security. pp. 4- ,(2011) , 10.1145/2016904.2016908
, Generative Adversarial Nets neural information processing systems. ,vol. 27, pp. 2672- 2680 ,(2014) , 10.3156/JSOFT.29.5_177_2
Quentin Jerome, Kevin Allix, Radu State, Thomas Engel, Using opcode-sequences to detect malicious Android applications international conference on communications. pp. 914- 919 ,(2014) , 10.1109/ICC.2014.6883436
Soumith Chintala, Alec Radford, Luke Metz, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks arXiv: Learning. ,(2015)
Ying Tan, Weiwei Hu, Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN arXiv: Learning. ,(2017)
Manzhi Yang, Qiaoyan Wen, Detecting android malware by applying classification techniques on images patterns international conference on cloud computing. pp. 344- 347 ,(2017) , 10.1109/ICCCBDA.2017.7951936
Aziz Makandar, Anita Patrot, Malware class recognition using image processing techniques 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI). pp. 76- 80 ,(2017) , 10.1109/ICDMAI.2017.8073489
Wei Guo, Tenghai Wang, Jizeng Wei, Malware Detection with Convolutional Neural Network Using Hardware Events CCF National Conference on Compujter Engineering and Technology. pp. 104- 115 ,(2017) , 10.1007/978-981-10-7844-6_11
ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb, MalDozer: Automatic framework for android malware detection using deep learning Digital Investigation. ,vol. 24, ,(2018) , 10.1016/J.DIIN.2018.01.007
Jin-Young Kim, Seok-Jun Bu, Sung-Bae Cho, Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders Information Sciences. pp. 83- 102 ,(2018) , 10.1016/J.INS.2018.04.092