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Robust genetic machine learning ensemble model for intrusion detection in network traffic.

Muhammad Ali Akhtar1, Syed Muhammad Owais Qadri2, Maria Andleeb Siddiqui3

  • 1Department of Computer and Information System Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

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This study introduces a novel Robust genetic ensemble classifier for network intrusion detection, significantly improving accuracy and reducing errors compared to existing machine learning methods. The enhanced algorithm offers a more reliable solution for detecting cyber threats in network traffic.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Rapid advancements in internet and communication technologies have led to increased cyber-attacks, overwhelming traditional network security systems.
  • Current Intrusion Detection Systems (IDS) struggle with false alerts, inability to prevent attacks autonomously, and difficulty detecting novel intrusions.
  • Machine learning (ML) based IDS are emerging as promising solutions for effective network intrusion detection.

Purpose of the Study:

  • To enhance network intrusion detection by developing highly reliable algorithms through preprocessing and ensemble methods.
  • To evaluate the performance of a proposed Robust genetic ensemble classifier against other machine learning ensemble algorithms.
  • To address the limitations of existing IDS in accuracy, false alarm rates, and detection of new threats.

Main Methods:

  • Utilized a combined data analysis technique with four robust machine learning ensemble algorithms: Voting Classifier, Bagging Classifier, Gradient Boosting Classifier, and Random Forest-based Bagging.
  • Developed and tested models for each algorithm using a Network Dataset.
  • Proposed and evaluated a novel Robust genetic ensemble classifier.

Main Results:

  • The proposed Robust genetic ensemble classifier demonstrated superior performance compared to other tested methods.
  • The suggested algorithm achieved the lowest values for Mean Square Error (MSE) and Mean Absolute Error (MAE).
  • Performance graphs confirmed the effectiveness of the algorithms in anticipating anomaly occurrences.

Conclusions:

  • The Robust genetic ensemble classifier is a highly effective method for enhancing network intrusion detection.
  • The proposed approach offers a significant improvement in accuracy and reliability over existing machine learning techniques.
  • Future work can explore integrating more machine learning ensemble classifiers and deep learning techniques for further advancements.