Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks
<|reference_start|>Enhancing Network Intrusion Detection Performance using Generative Adversarial Networks: Network intrusion detection systems (NIDS) play a pivotal role in safeguarding critical digital infrastructures against cyber threats. Machine learning-based detection models applied in NIDS are prevalent today. However, the effectiveness of these machine learning-based models is often limited by the evolving and sophisticated nature of intrusion techniques as well as the lack of diverse and updated training samples. In this research, a novel approach for enhancing the performance of an NIDS through the integration of Generative Adversarial Networks (GANs) is proposed. By harnessing the power of GANs in generating synthetic network traffic data that closely mimics real-world network behavior, we address a key challenge associated with NIDS training datasets, which is the data scarcity. Three distinct GAN models (Vanilla GAN, Wasserstein GAN and Conditional Tabular GAN) are implemented in this work to generate authentic network traffic patterns specifically tailored to represent the anomalous activity. We demonstrate how this synthetic data resampling technique can significantly improve the performance of the NIDS model for detecting such activity. By conducting comprehensive experiments using the CIC-IDS2017 benchmark dataset, augmented with GAN-generated data, we offer empirical evidence that shows the effectiveness of our proposed approach. Our findings show that the integration of GANs into NIDS can lead to enhancements in intrusion detection performance for attacks with limited training data, making it a promising avenue for bolstering the cybersecurity posture of organizations in an increasingly interconnected and vulnerable digital landscape.<|reference_end|>
@article{zhao2024enhancing,
    title={Enhancing Network Intrusion Detection Performance using Generative
  Adversarial Networks},
    author={Xinxing Zhao, Kar Wai Fok and Vrizlynn L. L. Thing},
    journal={arXiv preprint arXiv:2404.07464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07464},
    primaryClass={cs.CR}
}