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DeepIDX: sophisticated IDS model using the generative adversarial network (GAN) algorithm

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Abstract

Many security threats across the network make the security aspect the most critical security issue. To solve the problem, we propose an intrusion detection system (IDS) using Generative Adversarial Networks (GAN) by analyzing the extracted features of the network traffic. At present, the intrusion detection system is a widely used practical security tool to prevent malicious traffic from penetrating networks and systems. In this paper, we propose an intrusion detection model using GAN. To build our detection model, we collect the dataset, process it first, train it with several different parameters to get the highest accuracy results, then test the model using the new data. Based on experimental results, this model shows a threshold value of 0.0054779826. Therefore, a smaller threshold value indicates a more accurate detection model and a lower error rate.

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Authors and Affiliations

  1. Universitas Respati Yogyakarta, Depok, Indonesia

    Nurhadi Wijaya, Marselina Endah Hiswati & Sarah Anjani

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  1. Nurhadi Wijaya
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  2. Marselina Endah Hiswati
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  3. Sarah Anjani
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Correspondence to Nurhadi Wijaya.

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Wijaya, N., Hiswati, M.E. & Anjani, S. DeepIDX: sophisticated IDS model using the generative adversarial network (GAN) algorithm. Iran J Comput Sci 5, 197–204 (2022). https://doi.org/10.1007/s42044-022-00099-5

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  • DOI: https://doi.org/10.1007/s42044-022-00099-5

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