On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus
Adversarial examples for malware detection 10493 (2017), pp. 62-79 View at publisherCrossrefView in ScopusGoogle Scholar 31. D. Arp, M. Spreitzenbarth, M. Huebner, H. Gascon, K. Rieck Drebin: Efficient and explainable detection of android malware in your pocket 14 (2014), pp. 23-26 ...
using a substitute detector as a basic malware detector and the basic GAN method in training, to generate adversarial malware examples for attacking ML detectors
对抗机器学习:Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples,程序员大本营,技术文章内容聚合第一站。
Adversarial Examples In Machine Learning Explained How to fool a 27M-parameter model with a bit of Python Do you think it is impossible to fool the vision system of a self-driving Tesla car? Or that machine learning models used in malware detection software are too good to be evaded by ...
(DNN) by incorporating two defense techniques: adversarial training and ensemble (i.e., adversarial deep ensemble for short). The hardened DNNs are applied to an interesting context: adversarial mawlare detection. More specifically, we consider the Android malware examples. The main features of ...
自对抗样本(adversarial example)于2013年被提出以来,已经过去了6年时间。对抗样本的研究由最初的基于L-BFGS优化的针对图片分类器的对抗样本,演变为了目标识别,情感分析,机器Q&A,malware,以及语音识别等场景下的样本。制作对抗样本的算法也有了一系列革新。面对层出不穷的对抗样本攻击,目前的防御方法很难做到面面俱到。
(Han et al. 2013;Yang et al. 2018;Nataraj et al. 2011) have proved that the image of binary code could be used for malware detection, we innovatively generate adversarial examples based on binary images to evade malware detectors. First, the binary malware is processed as an image by a ...
Adversarial examples for malware detection European symposium on research in computer security, Springer (2017), pp. 62-79 CrossrefView in ScopusGoogle Scholar [17] Pierazzi F, Pendlebury F, Cortellazzi J, Cavallaro L. Intriguing properties of adversarial ML attacks in the problem space. In: ...
Adversarial examples for malware detection European Symposium on Research in Computer Security, Springer (2017), pp. 62-79 CrossrefView in ScopusGoogle Scholar [9] Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A. Towards deep learning models resistant to adversarial attacks. In: Sixth Inter...