Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

Asmaa Benchama; Khalid Zebbara

doi:10.56294/dm2023202

Original

Published: 2023-12-28

DOI: https://doi.org/10.56294/dm2023202

Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

Abstract

This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99.16% for multi-class classification and 99.10% for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security.

Keywords:

IDS,

GAN,

MSCNN,

BiLSTM,

LIME,

Hogzilla Dataset,

How to Cite

Benchama A, Zebbara K. Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions. Data and Metadata [Internet]. 2023 Dec. 28 [cited 2024 Jul. 3];2:202. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/202

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions

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