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A novel deep learning-based framework with particle swarm optimisation for intrusion detection in computer networks.

Abdullah Asım Yılmaz¹

¹Computer Engineering Department, Atılım University, Ankara, Turkey.

|February 12, 2025

Summary

This study introduces a novel deep learning hybrid architecture for network intrusion detection. The system enhances accuracy and efficiency in identifying cyber threats, outperforming existing methods.

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Network intrusion detection is crucial for information security.
Accurate identification of diverse and evolving cyber-attacks remains a significant challenge.
Existing intrusion detection systems often lack robustness against novel threats.

Purpose of the Study:

To propose a novel deep learning-based hybrid architecture for enhanced network intrusion detection.
To improve the efficiency and accuracy of identifying various intrusion types.
To develop a robust system capable of detecting contemporary cyber-attacks.

Main Methods:

A hybrid deep learning architecture combining three pre-trained network models.
Particle Swarm Optimization (PSO) for hyperparameter tuning.
A six-stage methodology: data gathering, pre-processing, DNN design, hyperparameter optimization, training, and evaluation.

Main Results:

The proposed model achieved high accuracy rates on benchmark datasets: 93.55% on KDDCUP'99, 90.42% on NSL-KDD, and 82.44% on UNSW-NB15.
Demonstrated superior performance compared to alternative state-of-the-art intrusion detection schemes.
Successfully classified different types of network intrusions with high precision.

Conclusions:

The novel deep learning hybrid architecture offers a significant advancement in network intrusion detection.
The proposed method provides a robust and accurate solution for identifying evolving cyber threats.
This approach effectively enhances the overall security posture of computer networks.