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.

Input: Consider Input: 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.

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

Input: Consider Input: In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.

Output: Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

Input: Consider Input: Davoud Mougouei School of Computer Science, Engineering, and Mathematics Flinders University Adelaide, Australia davoud.mougouei@inders.edu.au David M. W. Powers School of Computer Science, Engineering, and Mathematics Flinders University Adelaide, Australia david.powers@inders.edu.au Asghar Moeini School of Computer Science, Engineering, and Mathematics Flinders University Adelaide, Australia asghar.moeini@inders.edu.au
Output: An Integer Programming Model for Binary Knapsack Problem with Value-Related Dependencies among Elements