Recently Command and Control (C&C) servers have attracted considerable attention in botnets and domain generation algorithms (DGAs) further enhance the stealth of C&C servers. However, Algorithmically Generated Domains (AGDs) generated by DGAs can be easily detected by previous DGA detection approaches. More specifically, the previous DGAs are hard to satisfy domain name rules, low repetition rate, and anti-detection in practical scenarios simultaneously. Designing an outstanding DGA has become a crucial issue from the botnet owner’s perspective. To mitigate these problems, we propose Cdga, a Controllable DGA via Generative Adversarial Networks (GAN), which is a popular backbone model for text generation in the natural language processing (NLP) community.Controllable text generation approaches are adopted by Cdga to ensure no repetition in the generated domain names and compliance with the domain rules. In addition to cheating DGA detectors, GANs are exploited to equip Cdga with a powerful anti-detection ability. Furthermore, our proposed method uses the technique of NLP to force the AGDs to meet language rules, where the generated domain names are difficult for recognition by human. By utilizing the time-dependent seed, Cdga can dynamically generate domain names, ensuring that the malware can connect to the C&C server conditioned on a specific time stamp. Experimental results demonstrate that the domain names generated by our method are realistic enough to be resistant to the state-of-the-art DGA detectors.

Recently, the person re-identification task becomes increasingly crucial in crowded scenarios, e.g., airports and schools. Many methods with high performance have been proposed to solve this problem. However, the existence of occlusion still challenges the development of person re-identification. In this work, we present the novel Pose - Guided Mixed Attention Network (PGMANet), an end-to-end framework to deal with pedestrian reidentification under occluded situations by fusing posture and second-order information. Specially, we employ two models. The first is Human Part - level Attention Model. We use key point information of a pedestrian to generate a heat map to enhance the pedestrian body part's feature. Simultaneously, we design Second - order Information Attention Model to investigate the correlation among features of different parts. Experimental results show that our method achieves state-of-the-art person re-identification performance on two challenging occlusion datasets Occluded-DukeMTMC and Occluded-Reid.