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Related Experiment Video

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Network Anomaly Intrusion Detection Based on Deep Learning Approach.

Yung-Chung Wang1, Yi-Chun Houng1, Han-Xuan Chen1

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan.

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|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study evaluates deep learning models for network intrusion detection using the CSE-CIC-IDS2018 dataset. Individual models like DNN, RNN, and CNN show high accuracy and faster inference, making them suitable for intrusion detection systems (IDS).

Keywords:
data processingdeep learningnetwork intrusion detection

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Internet traffic analysis is crucial for detecting cyberattacks.
  • Deep learning shows promise for intrusion detection systems (IDS).
  • Existing research often uses outdated datasets, limiting the detection of current threats.

Purpose of the Study:

  • To evaluate the effectiveness of various deep learning models for network intrusion detection.
  • To assess model performance on a current dataset (CSE-CIC-IDS2018).
  • To compare individual models against combined models for practical IDS implementation.

Main Methods:

  • Preprocessing the CSE-CIC-IDS2018 dataset.
  • Constructing and evaluating six deep learning models: DNN, CNN, RNN, LSTM, CNN + RNN, and CNN + LSTM.
  • Conducting binary and multi-class classification experiments to identify benign and malicious traffic (BruteForce, DoS, Web Attacks, Infiltration, Botnet, DDoS).

Main Results:

  • All evaluated models achieved high accuracy, with multi-class classification exceeding 98%.
  • Individual models (DNN, RNN, CNN) demonstrated competitive detection performance.
  • Combined models (CNN + RNN, CNN + LSTM) exhibited longer inference times compared to individual models.

Conclusions:

  • Deep learning models, particularly DNN, RNN, and CNN, are effective for network intrusion detection.
  • Individual models offer a better balance of performance and efficiency for IDS implementation.
  • The study highlights the importance of using current datasets for evaluating intrusion detection mechanisms.