ArticleMethodsX2025
Time-frequency analysis and autoencoder approach for network traffic anomaly detection.
Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- TFPAG-Net: A Time-Frequency Dual-Branch Fusion and PCMCI-Based Association-Guided Network for IIoT Intrusion Detection.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of anomalies in network traffic is critical to mitigating cyber threats. This study integrates continuous wavelet transform (CWT), discrete-time Fourier transform (DTFT), short-time Fourier transform (STFT), and autoencoders to identify anomalous network behaviour. It conducts time- frequency analysis of pre-processed network traffic data such as packet size and duration, extracting meaningful features fed into an autoencoder. Reconstruction error deviations indicate anomalies like spikes or irregular oscillations.•This hybrid approach demonstrates good scalability for the real-time implementation of cybersecurity measures. Further developments can be made in autoencoder architectures to achieve their full potential in large-scale systems.•The model is robust and scalable for real-time applications, achieving 95% detection accuracy by identifying 72 anomalies.•Obtained results indicate that the approach is feasible for deploying in practical cybersecurity applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.