Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
暂无分享,去创建一个
Abdullah Al-Dujaili | Una-May O'Reilly | Erik Hemberg | Alex Huang | Una-May O’Reilly | Alex Huang | Erik Hemberg | Abdullah Al-Dujaili
[1] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[2] Carl A. Gunter,et al. Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps , 2017, ACSAC.
[3] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[4] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[5] Patrick D. McDaniel,et al. Adversarial Examples for Malware Detection , 2017, ESORICS.
[6] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[7] Lior Rokach,et al. Generic Black-Box End-to-End Attack against RNNs and Other API Calls Based Malware Classifiers , 2017, ArXiv.
[8] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[9] Jack W. Stokes,et al. Large-scale malware classification using random projections and neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[10] Chia-Mu Yu,et al. R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections , 2017, 2018 IEEE International Conference on Big Data (Big Data).
[11] Konstantin Berlin,et al. Deep neural network based malware detection using two dimensional binary program features , 2015, 2015 10th International Conference on Malicious and Unwanted Software (MALWARE).
[12] Fabio Roli,et al. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2018, CCS.
[13] Salvatore J. Stolfo,et al. Data mining methods for detection of new malicious executables , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[14] Edward Raff,et al. Learning the PE Header, Malware Detection with Minimal Domain Knowledge , 2017, AISec@CCS.
[15] J. Doug Tygar,et al. Adversarial machine learning , 2019, AISec '11.
[16] Hung Dang,et al. Evading Classifiers by Morphing in the Dark , 2017, CCS.
[17] Burton H. Bloom,et al. Space/time trade-offs in hash coding with allowable errors , 1970, CACM.
[18] Lior Rokach,et al. Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers , 2017, RAID.
[19] Saibal Mukhopadhyay,et al. Cascade Adversarial Machine Learning Regularized with a Unified Embedding , 2017, ICLR.
[20] Ying Tan,et al. Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN , 2017, DMBD.
[21] Patrick D. McDaniel,et al. Adversarial Perturbations Against Deep Neural Networks for Malware Classification , 2016, ArXiv.
[22] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[23] David Wagner,et al. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.
[24] H. Anderson,et al. Evading Machine Learning Malware Detection , 2017 .
[25] Valentina Zantedeschi,et al. Efficient Defenses Against Adversarial Attacks , 2017, AISec@CCS.