Integrating GAN-Generated Data into Real-Time IDS Workflows: A Practical Study
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22649Keywords:
Generative Adversarial Networks (GANs), Intrusion Detection Systems (IDS), Real-Time Data Augmentation, Class Imbalance, Synthetic Data Generation, Suricata IntegrationAbstract
Class imbalance occurs in intrusion detection systems (IDS) when the level of benign traffic is oversampled, whereas rare attacks, such as U2R and R2L, are undersampled, causing biased classifiers to generate high false-negative rates. This study proposes a novel architecture that enables real-time synthetic data generation and augmentation with integration into existing IDS of an additional module based on Generative Adversarial Networks (GANs). The generator proposed in the GAN variant consists of sequential generative layers with linear activations and reaches (ReLU), with a discriminator whose activation is LeakyReLU and binary cross-entropy loss. Data on minority classes is synthesized with the help of the datasets such as NSL-KDD, CIC-IDS-2017, and CIC-IDS-2018. These features are performed by the architecture to monitor asynchronous traffic using tools (for example, Suricata and tcpdump), GAN activation in response to anomalies, and incremental updates on classifiers (for example, Random Forest) to enable minimal operations. The accuracy of prototypical validation reached up to 98%, and F1 score improvements between 3-5% on minority classes of low latency level of less than 2 s per batch under the acceleration of a graphics card. The early assessments show positive progress regarding the detection performance, cost-effectiveness of the computations, and possible scalability to more modern types of threat, which can be further expanded by developing conditional GANs and federated learning in various areas of cybersecurity.
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Copyright (c) 2026 Zainab A. Abdulazeez, Israa Abdulkadhim Jabbar Al Ali, Basma Mustafa M. H.
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