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.
use real network traffic as input and combine it with synthetically created attacks. FLAME @cite_42 is a generator for malicious network traffic. The authors use rule-based approaches to inject e.g. attacks or attacks. @cite_12 present ID2T, a similar approach which combines real network traffic with synthetically created malicious network traffic. For creating malicious network traffic, the authors use rule-based scripts or manipulate parameters of the input network traffic. @cite_20 analyze attacks on flow level and use a Hidden Markov Model to model the characteristics of them. However, their model generates only the number of , and during a typical attack scenario and does not generate complete flow-based data.
@cite_42 @cite_20 @cite_12
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There are several remaining open questions in the area of flow-based anomaly detection, e.g., how to do meaningful evaluations of anomaly detection mechanisms; how to get conclusive information about the origin and nature of an anomaly; or how to detect low intensity attacks. In order to answer these questions, network traffic traces that are representative for a specific test environment, and that contain anomalies with selected characteristics are a prerequisite. In this work, we present flame, a tool for injection of hand-crafted anomalies into a given background traffic trace. This tool combines the controllability offered by simulation with the realism provided by captured traffic traces. We present the design and prototype implementation of flame, and show how it is applied to inject three example anomalies into a given flow trace. We believe that flame can contribute significantly to the development and evaluation of advanced anomaly detection mechanisms. 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. Nowadays, network load is constantly increasing and high-speed infrastructures (1-10Gbps) are becoming increasingly common. In this context, flow-based intrusion detection has recently become a promising security mechanism. However, since flows do not provide any information on the content of a communication, it also became more difficult to establish a ground truth for flow-based techniques benchmarking. A possible approach to overcome this problem is the usage of synthetic traffic traces where the generation of malicious traffic is driven by models. In this paper, we propose a flow time series model of SSH brute-force attacks based on Hidden Markov Models. Our results show that the model successfully emulates an attacker behavior, generating meaningful flow time series. We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.