In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Machine learning has been used to detect new malware in recent years, while malware authors have strong motivation to attack such algorithms.Malware authors usually have no access to the detailed structures and parameters of the machine learning models used by malware detection systems, and therefore they can only perform black-box attacks. This paper proposes a generative adversarial network (GAN) based algorithm named MalGAN to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models. MalGAN uses a substitute detector to fit the black-box malware detection system. A generative network is trained to minimize the generated adversarial examples’ malicious probabilities predicted by the substitute detector. The superiority of MalGAN over traditional gradient based adversarial example generation algorithms is that MalGAN is able to decrease the detection rate to nearly zero and make the retraining based defensive method against adversarial examples hard to work.
[EX A]: Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

[EX Q]: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.
[EX A]: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

[EX Q]: We apply multilayer bootstrap network (MBN), a recent pro-<lb>posed unsupervised learning method, to unsupervised speaker<lb>recognition. The proposed method first extracts supervectors<lb>from an unsupervised universal background model, then re-<lb>duces the dimension of the high-dimensional supervectors by<lb>multilayer bootstrap network, and finally conducts unsuper-<lb>vised speaker recognition by clustering the low-dimensional<lb>data. The comparison results with 2 unsupervised and 1 su-<lb>pervised speaker recognition techniques demonstrate the ef-<lb>fectiveness and robustness of the proposed method.
[EX A]:
MULTILAYER BOOTSTRAP NETWORK FOR UNSUPERVISED SPEAKER RECOGNITION