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Advancements in intrusion detection: A lightweight hybrid RNN-RF model.

Nasrullah Khan¹, Muhammad Ismail Mohmand¹, Sadaqat Ur Rehman²

¹Department of Computer Science Brains Institute, Peshawar, Pakistan.

|June 21, 2024

Summary

This study introduces a novel intrusion detection system using Recurrent Neural Networks (RNNs) for enhanced data preprocessing and feature extraction. The method achieves high accuracy, improving network security against evolving cyber threats.

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Area of Science:

Computer Science
Cybersecurity
Machine Learning

Background:

Computer networks are vulnerable to diverse attacks, threatening data security and communication freedom.
Traditional intrusion detection methods often struggle with complex, evolving threats.

Purpose of the Study:

To introduce a novel intrusion detection technique leveraging Recurrent Neural Networks (RNNs).
To enhance data preprocessing and feature extraction for improved network security.

Main Methods:

Utilizing RNNs for data preprocessing and feature extraction from hidden layers.
Applying various classification algorithms (Decision Tree, Random Forest, CatBoost) post-feature extraction.
Reversing the conventional sequence of training and feature extraction for a modified pipeline.

Main Results:

Achieved 99.6% accuracy on the Network Security Laboratory (NSL) dataset.
Achieved 99.8% and 99.9% accuracy on the Canadian Institute for Cybersecurity (CIC) 2017 dataset.
Demonstrated substantial practical implications of RNNs in intrusion detection.

Conclusions:

The novel RNN-based approach significantly enhances intrusion detection capabilities.
The modified pipeline offers a major shift in network security methodologies.
This technique is crucial for safeguarding data security and communication freedom against modern network threats.