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Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic

Gang Long¹, Zhaoxin Zhang¹

¹Faculty of Computing, Harbin Institute of Technology, Harbin 150000, China.

Computational Intelligence and Neuroscience

|March 20, 2023

Summary

This summary is machine-generated.

This study introduces Deep Encrypted Traffic Detection (DETD), a novel framework for efficient encrypted traffic anomaly detection. DETD significantly enhances feature extraction efficiency and achieves near-perfect anomaly detection performance.

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Area of Science:

Computer Science
Network Security
Artificial Intelligence

Background:

Increasing network attacks utilize encrypted communication, making anomaly detection crucial for network security.
Existing encrypted traffic anomaly detection methods suffer from low efficiency due to feature extraction challenges.

Purpose of the Study:

To propose an efficient framework for encrypted traffic anomaly detection using parallel automatic feature extraction.
To enhance the accuracy and efficiency of detecting anomalies in encrypted network traffic.

Main Methods:

Developed Deep Encrypted Traffic Detection (DETD), a framework employing parallel small-scale multilayer stack autoencoders for local feature extraction.
Utilized an L1 regularization-based feature selection algorithm to identify the most representative feature set.
Implemented DETD for encrypted traffic anomaly detection.

Main Results:

DETD demonstrated a 66% higher feature extraction efficiency compared to conventional stacked autoencoders.
Achieved a high anomaly detection performance rate of 99.998%.
DETD outperformed existing deep full-range frameworks and other neural network-based anomaly detection algorithms.

Conclusions:

DETD offers a robust and highly efficient solution for encrypted traffic anomaly detection.
The parallel automatic feature extraction approach significantly improves performance and efficiency.
DETD represents a substantial advancement in securing networks against encrypted malicious traffic.