Static Analysis Approach of Malware Using Machine Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9811-1_9
Reference21 articles.
1. Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. IEEE Trans Inf Forensics Secur 14(3):773–788
2. Karimipour H, Dehghantanha A, Parizi RM, Choo KKR, Leung H (2019) A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7:80778–80788
3. Luo S, Peng A, Zeng H, Kang X, Liu L (2019) Deep residual learning using data augmentation for median filtering forensics of digital images. IEEE Access 7:80614–80621
4. Varma A, Ravi Kiran P, Raj KP, Subba Raju KV (2017) Android mobile security by detecting and classification of malware based on permissions using machine learning algorithms. In: 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE
5. Kornish D et al (2018) Malware classification using deep convolutional neural networks. In: 2018 IEEE applied imagery pattern recognition workshop (AIPR). IEEE
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