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A new intrusion detection method using ensemble classification and feature selection
Pooyan Azizi Doost1, Sadegh Sarhani Moghadam2, Edris Khezri3
1Khuzestan Electric Power Distribution Company, Shahid Monsefi Ave, Ahvaz, Amanieh, Iran. p.azizidoost@gmail.com.
Scientific Reports
|April 20, 2025
Summary
This study presents a hybrid intrusion detection system (IDS) using Convolutional Neural Networks (CNNs) and Random Forest (RF) for enhanced network security. The novel approach achieves high accuracy in identifying cyber threats, offering a scalable cybersecurity solution.
Area of Science:
- Cybersecurity
- Network Security
- Machine Learning
Background:
- Intrusion Detection Systems (IDS) are vital for network security.
- Traditional IDS methods face challenges with complex cyber threats.
- Need for advanced techniques to improve threat detection accuracy.
Purpose of the Study:
- To develop a hybrid IDS combining CNNs and RF.
- To enhance intrusion detection accuracy and efficiency.
- To provide a scalable cybersecurity solution for real-world networks.
Main Methods:
- Utilized Convolutional Neural Networks (CNNs) for automatic feature extraction.
- Employed the Random Forest (RF) algorithm for robust classification.
- Validated the approach on KDD99 and UNSW-NB15 datasets.
Main Results:
- Achieved 97% accuracy and over 98% precision.
- Demonstrated superior performance compared to traditional IDS.
- CNNs effectively reduced data dimensionality and noise.
Conclusions:
- The hybrid CNN-RF model offers a powerful and efficient IDS.
- The approach shows significant potential for real-world network security.
- This framework represents a scalable solution for mitigating cyber threats.
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