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Updated: May 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

DELP-Net: A Differentiable Entropy Layer Pyramid Network for End-to-End Low-Rate DoS Detection.

Jinyi Wang¹, Congyuan Xu^1,2, Jun Yang¹

¹College of Artificial Intelligence, Jiaxing University, Jiaxing 314001, China.

Entropy (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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This study introduces DELP-Net, a novel network for detecting stealthy low-rate Denial-of-Service (LDoS) attacks. DELP-Net effectively identifies LDoS traffic patterns, enhancing network security against sophisticated threats.

Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

Low-rate Denial-of-Service (LDoS) attacks are stealthy threats that exploit periodic traffic pulses.
Traditional detection methods struggle to differentiate LDoS traffic from legitimate network activity due to its low average rate and bursty nature.

Purpose of the Study:

To propose DELP-Net, an end-to-end Differentiable Entropy Layer Pyramid Network for online LDoS detection.
To develop a robust method for identifying LDoS attacks directly from raw network traffic at the window level.

Main Methods:

DELP-Net integrates a multi-scale convolutional pyramid with a differentiable Rényi-entropy attention mechanism.
It employs an entropy-conditioned temporal convolutional network for modeling cross-window dependencies and an entropy-regularized hybrid loss function for robustness.

Keywords:

Rényi entropy cybersecurity uncertainty deep learning intrusion detection pyramid fusion

Related Experiment Videos

Last Updated: May 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Main Results:

DELP-Net achieved an average F1 score of 0.9877 across six LDoS attack types.
The system demonstrated a high detection rate of 98.69% with a low false-positive rate of 1.15% on the LDoS dataset.

Conclusions:

DELP-Net is highly effective for online LDoS detection, outperforming existing methods.
The proposed network is suitable for practical deployment in intrusion detection systems for enhanced network security.