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

TL;DR

This paper proposes a novel intrusion detection system combining GANs, MSCNNs, BiLSTM, and LIME to generate data, detect intrusions, and explain decisions, achieving high accuracy and interpretability on benchmark datasets.

Contribution

It introduces an integrated deep learning framework with interpretability for intrusion detection, combining data synthesis, multi-scale feature extraction, temporal analysis, and explanation.

Findings

Achieved 99.16% accuracy in multi-class intrusion detection.

Demonstrated high interpretability with LIME explanations.

Validated effectiveness on the Hogzilla dataset.

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.