Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks

Wu, Chao-Lun; Chen, Yuying; Chou, Po-Yu; Wang, Chih-Yu

doi:10.1109/globecom48099.2022.10001157

Cited by 2 publications

(3 citation statements)

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“…The results indicated that the distribution of the generated samples was very close to the real ones. Furthermore, the Frechet Traffic Distance (FTD) score indicated that the distribution of synthetically generated data was, also, very close to other random distributions [13].…”

Section: Related Workmentioning

confidence: 88%

“…Moreover, it achieved a better scalability-fidelity trade-off compared to other existing solutions [12]. In a similar direction, Wu et al [13] proposed the Synthetic Packet Traffic Generative Adversarial Network (SPATGAN) approach. The SPATGAN framework consisted of a server agent and a client agent to simulate/mimic the exchange of packets over a network.…”

Section: Related Workmentioning

confidence: 97%

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A Comparison Study of Generative Adversarial Network Architectures for Malicious Cyber-Attack Data Generation

et al. 2023

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The digitization trend that prevails nowadays has led to increased vulnerabilities of tools and technologies of everyday life. One of the many different types of software vulnerabilities and attacks is botnets. Botnets enable attackers to gain remote control of the infected machines, often leading to disastrous consequences. Cybersecurity experts engage machine learning (ML) and deep learning (DL) technologies for designing and developing smart and proactive cybersecurity systems in order to tackle such infections. The development of such systems is, often, hindered by the lack of data that can be used to train them. Aiming to address this problem, this study proposes and describes a methodology for the generation of botnet-type data in tabular format. This methodology involves the design and development of two generative adversarial network (GAN) models, one with six layers and the other with eight layers, to identify the most efficient and reliable one in terms of the similarity of the generated data to the real ones. The two GAN models produce data in loops of 25, 50, 100, 250, 500 and 1000 epochs. The results are quite encouraging as, for both models, the similarity between the synthetic and the real data is around 80%. The eight-layer solution is slightly better as, after running for 1000 epochs, it achieved a similarity degree of 82%, outperforming the six-layer one, which achieved 77%. These results indicate that such solutions of data augmentation in the cybersecurity domain are feasible and reliable and can lead to new standards for developing and training trustworthy ML and DL solutions for detecting and tackling botnet attacks.

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Section: Related Workmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 97%

A Comparison Study of Generative Adversarial Network Architectures for Malicious Cyber-Attack Data Generation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…They either emulate traffic patterns using probability distributions, which may not accurately represent real-world packet traffic, or they require an extensive setup of targeted applications with user modeling, which can be a challenging and time-consuming process. Wu et al [20] point out that while some traffic generators offer advanced distributions that account for bursty traffic and anomalies, the actual bottleneck is that packet lengths in real-life network traffic are categorical (i.e., limited to specific values or ranges like IP addresses and port numbers), not continuous. Categorical data refers to data that can be divided into distinct categories or bins, while continuous values are those that can take any value within a given range.…”

Section: A Chronological Survey Of Network Traffic Generatorsmentioning

confidence: 99%

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

Kholgh,

Kostakos

2023

IEEE Access

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The application of machine learning models, particularly in cybersecurity, has surged significantly in the past few years. However, the effectiveness of these models is predominantly tethered to the quality and breadth of the training data they ingest. The scarcity of realistic datasets within the cybersecurity field constitutes a considerable challenge to the development of industry-grade tools intended for real-world application scenarios. Specifically, current datasets are either significantly outdated or fall short on both qualitative and quantitative fronts, primarily because many organizations exhibit reluctance in data sharing, stemming from privacy concerns or the potential threat to trade secrets. To address this challenge, the paper introduces PAC-GPT, a novel framework to generate reliable synthetic data for machine learning methods based on Open AI's Generative Pre-trained Transformer 3 (GPT-3). The core components of this framework are two modules, namely a Flow Generator, which is responsible for capturing and regenerating patterns in a series of network packets, and Packet Generator, which can generate individual network packets given the network flow. We also propose a packet generator based on LLM chaining and then proceed to assess, compare, and evaluate its performance using metrics such as loss, accuracy and success rate, concluding that transformers are a suitable approach for synthetic packet generation with minimal fine-tuning performed. Lastly, a streamlined command line interface (CLI) tool has been devised to facilitate the seamless access of this innovative data generation strategy by professionals from various disciplines.

show abstract

Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks

Cited by 2 publications

References 10 publications

A Comparison Study of Generative Adversarial Network Architectures for Malicious Cyber-Attack Data Generation

A Comparison Study of Generative Adversarial Network Architectures for Malicious Cyber-Attack Data Generation

PAC-GPT: A Novel Approach to Generating Synthetic Network Traffic With GPT-3

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