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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.

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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.

Keywords:
Intrusion detection system (IDS)Machine learningNeural networksRandom forest

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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.