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.
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Question: 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.

Answer: Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN


Question: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­ other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do­ ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de­ pendencies as those used by certain algorithms for learn­ ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for­ warded. Finally, methods for further testing the accu­ racy of these claims are discussed.

Answer: THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE


Question: Conditional probabilities are a core concept in machine learning. For ex-<lb>ample, optimal prediction of a label Y given an inputX corresponds to maximizing<lb>the conditional probability of Y given X . A common approach to inference tasks<lb>is learning a model of conditional probabilities. However, these models are often<lb>based on strong assumptions (e.g., log-linear models), and hence their estimate of<lb>conditional probabilities is not robust and is highly dependent on the validity of<lb>their assumptions.<lb>Here we propose a framework for reasoning about conditional probabilities without<lb>assuming anything about the underlying distributions, except knowledge of their<lb>second order marginals, which can be estimated from data. We show how this<lb>setting leads to guaranteed bounds on conditional probabilities, which can be calcu-<lb>lated efficiently in a variety of settings, including structured-prediction. Finally, we<lb>apply them to semi-supervised deep learning, obtaining results competitive with<lb>variational autoencoders.

Answer:
Robust Conditional Probabilities