Abstract Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.
The botnet, as one of the most formidable threats to cyber security, is often used to launch large-scale attack sabotage. How to accurately identify the botnet, especially to improve the performance of the detection model, is a key technical issue. In this paper, we propose a framework based on generative adversarial networks to augment botnet detection models (Bot-GAN). Moreover, we explore the performance of the proposed framework based on flows. The experimental results show that Bot-GAN is suitable for augmenting the original detection model. Compared with the original detection model, the proposed approach improves the detection performance, and decreases the false positive rate, which provides an effective method for improving the detection performance. In addition, it also retains the primary characteristics of the original detection model, which does not care about the network payload information, and has the ability to detect novel botnets and others using encryption or proprietary protocols. Generative Adversarial Networks (GANs) have been successfully used in a large number of domains. This paper proposes the use of GANs for generating network traffic in order to mimic other types of traffic. In particular, our method modifies the network behavior of a real malware in order to mimic the traffic of a legitimate application, and therefore avoid detection. By modifying the source code of a malware to receive parameters from a GAN, it was possible to adapt the behavior of its Command and Control (C2) channel to mimic the behavior of Facebook chat network traffic. In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server was deployed in the cloud. Results show that a GAN can successfully modify the traffic of a malware to make it undetectable. The modified malware also tested if it was being blocked and used this information as a feedback to the GAN. This work envisions the possibility of self-adapting malware and self-adapting IPS. 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.