A Continuous Learning Approach for Real-Time Network Intrusion Detection

Author:

Martina Marcello Rinaldo1,Foresti Gian Luca1

Affiliation:

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

Abstract

Network intrusion detection is becoming a challenging task with cyberattacks that are becoming more and more sophisticated. Failing the prevention or detection of such intrusions might have serious consequences. Machine learning approaches try to recognize network connection patterns to classify unseen and known intrusions but also require periodic re-training to keep the performances at a high level. In this paper, a novel continuous learning intrusion detection system, called Soft-Forgetting Self-Organizing Incremental Neural Network (SF-SOINN), is introduced. SF-SOINN, besides providing continuous learning capabilities, is able to perform fast classification, is robust to noise, and it obtains good performances with respect to the existing approaches. The main characteristic of SF-SOINN is the ability to remove nodes from the neural network based on their utility estimate. SF-SOINN has been validated on the well-known NSL-KDD and CIC-IDS-2017 intrusion detection datasets as well as on some artificial data to show the classification capability on more general tasks.

Funder

ONRG

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Network Intrusion Detection with Incremental Active Learning;Lecture Notes on Data Engineering and Communications Technologies;2024

2. Transformer‐optimized generation, detection, and tracking network for images with drainage pipeline defects;Computer-Aided Civil and Infrastructure Engineering;2023-01-05

3. Cloud computing network intrusion risk detection method under large-scale DDoS attack;2022 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS);2022-03

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