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A Continuous Learning Approach for Real-Time Network Intrusion Detection.

Marcello Rinaldo Martina1, Gian Luca Foresti1

  • 1Department of Mathematics, Computer Science, and Physics, University of Udine, Via delle, Scienze 206, Udine, 33100, Italy.

International Journal of Neural Systems
|November 15, 2021
PubMed
Summary
This summary is machine-generated.

A new continuous learning system, Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), enhances network intrusion detection. This novel approach offers fast, robust classification and removes unnecessary nodes for improved performance against sophisticated cyberattacks.

Keywords:
Machine learningcontinuous learningcybersecurityintrusion detection

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Sophisticated cyberattacks pose significant challenges to traditional network intrusion detection systems.
  • Existing machine learning methods require frequent retraining to maintain high performance levels.

Purpose of the Study:

  • Introduce a novel continuous learning intrusion detection system, Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN).
  • Address the limitations of periodic retraining in current machine learning-based intrusion detection.

Main Methods:

  • Developed SF-SOINN, a neural network system with continuous learning capabilities.
  • Implemented a node removal mechanism based on utility estimation within the SF-SOINN architecture.
  • Validated the system on NSL-KDD and CIC-IDS-2017 intrusion detection datasets.

Main Results:

  • SF-SOINN demonstrates continuous learning, fast classification, and robustness to noise.
  • Achieved competitive performance compared to existing network intrusion detection approaches.
  • Showcased classification capabilities on general tasks using artificial data.

Conclusions:

  • SF-SOINN offers an effective solution for continuous network intrusion detection.
  • The system's adaptive nature and efficient node management contribute to its strong performance.
  • SF-SOINN represents a promising advancement in combating evolving cyber threats.