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An optimized ensemble model with advanced feature selection for network intrusion detection.

Afaq Ahmed¹, Muhammad Asim², Irshad Ullah¹

¹School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.

Peerj. Computer Science

|December 9, 2024

Summary

This summary is machine-generated.

This study introduces Optimized Random Forest (Opt-Forest) for advanced network intrusion detection. Opt-Forest enhances security systems by effectively identifying sophisticated cyber threats, outperforming traditional methods.

Keywords:

Cybersecurity Ensemble models Feature selection Machine learning Network intrusion detection systems

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Modern networks face evolving cyber threats.
Traditional intrusion detection systems struggle with sophisticated attacks.
Need for resilient and adaptive Network Intrusion Detection Systems (NIDS).

Purpose of the Study:

To develop an enhanced NIDS model for improved threat detection.
To introduce the Optimized Random Forest (Opt-Forest) ensemble model.
To increase the adaptability and resilience of NIDS against contemporary cyber threats.

Main Methods:

Developed the Optimized Random Forest (Opt-Forest) model.
Integrated genetic algorithms (GAs) for decision forest construction.
Employed advanced feature selection: Best-First Search, PSO, Evolutionary Search, and Genetic Search.

Main Results:

Opt-Forest demonstrated superior performance compared to traditional ML models (AbM1, KNN, J48, MLP, SGD, NB, LMT).
The GA-based approach facilitated wider exploration and avoided local optima for more accurate trees.
Achieved enhanced accuracy and reduced false alarms in network intrusion detection.

Conclusions:

The Opt-Forest model significantly enhances NIDS capabilities.
Genetic algorithms improve decision tree accuracy and compactness.
Proposed method offers a robust solution for detecting evolving cyber threats.