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  11. Time-frequency Analysis And Autoencoder Approach For Network Traffic Anomaly Detection

Time-frequency analysis and autoencoder approach for network traffic anomaly detection

Ruchira Purohit1,2, Satish Kumar1,2, Sameer Sayyad1

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.

Methodsx
|March 19, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a hybrid model using time-frequency analysis and autoencoders for detecting network traffic anomalies. The scalable, robust approach achieves 95% accuracy in identifying cyber threats in real-time.

Area of Science:

  • Cybersecurity and Network Analysis
  • Machine Learning for Anomaly Detection

Background:

  • Effective detection of network traffic anomalies is crucial for mitigating cyber threats.
  • Existing methods may face challenges in scalability and real-time implementation for comprehensive cybersecurity.

Purpose of the Study:

  • To develop and evaluate a hybrid approach integrating time-frequency analysis and autoencoders for robust network anomaly detection.
  • To assess the scalability and real-time feasibility of the proposed model for practical cybersecurity applications.

Main Methods:

  • Network traffic data (packet size, duration) underwent pre-processing and time-frequency analysis using Continuous Wavelet Transform (CWT), Discrete-Time Fourier Transform (DTFT), and Short-Time Fourier Transform (STFT).
  • Extracted features were utilized to train an autoencoder model, with anomalies identified by deviations in reconstruction error.
Keywords:
Anomaly detectionAutoencodersContinuous wavelet transformDiscrete-time Fourier transform

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  • The hybrid model's performance was evaluated for scalability and real-time detection capabilities.

    • Main Results:

    • The hybrid approach demonstrated good scalability for real-time cybersecurity implementations.
    • The model achieved 95% detection accuracy, successfully identifying 72 network anomalies.
    • Reconstruction error deviations effectively indicated anomalies such as spikes and irregular oscillations in network traffic.

    Conclusions:

    • The developed hybrid model is robust and scalable for real-time cybersecurity applications.
    • The approach shows feasibility for deployment in practical cybersecurity scenarios.
    • Further enhancements in autoencoder architectures could optimize performance in large-scale systems.
    Hybrid Time-Frequency Analysis and Autoencoder
    Hybrid time-frequency analysis
    Network traffic
    Short-time Fourier transform