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A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model.

Muhammad Basit Umair¹, Zeshan Iqbal¹, Muhammad Ahmad Faraz²

¹Department of Computer Science, University of Engineering and Technology Taxila, Taxila, Pakistan.

|June 15, 2022

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This summary is machine-generated.

This study introduces a statistical approach for intrusion detection systems (IDS) to improve accuracy with large datasets. The proposed method achieved 99% accuracy, outperforming traditional methods in network security.

Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

Traditional intrusion detection systems (IDS) struggle with high accuracy due to large data volumes.
Existing methods often fail to effectively analyze complex network traffic patterns.

Purpose of the Study:

To propose a novel statistical approach for enhancing intrusion detection system accuracy.
To address the limitations of traditional methods in handling large-scale network data.

Main Methods:

Feature extraction and selection using a multilayer convolutional neural network.
Classification of network intrusions via a softmax classifier and a multilayer deep neural network.
Validation using NSL-KDD and KDDCUP'99 benchmark datasets.

Keywords:

CNN NSL-KDD dataset classification deep learning intrusion detection

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Main Results:

The proposed statistical approach achieved a high accuracy of 99%.
Performance metrics including recall, F1-score, and precision were used for evaluation.
Superior performance demonstrated compared to existing intrusion detection systems.

Conclusions:

The statistical approach offers a significant improvement for intrusion detection.
The model effectively classifies network intrusions with high accuracy.
This research provides a robust solution for modern network security challenges.