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Anomaly detection in encrypted network traffic using self-supervised learning.

Sadaf Sattar¹, Shumaila Khan², Muhammad Ismail Khan³

¹Department of Computer Science and Information Technology, The Superior University, Lahore, Pakistan.

Scientific Reports

|July 22, 2025

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This summary is machine-generated.

ET-SSL, a novel approach for encrypted network traffic anomaly detection, uses self-supervised contrastive learning. It achieves high accuracy without labeled data, offering efficient, real-time detection for enhanced privacy and security.

Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

Traditional anomaly detection methods fail with encrypted traffic due to payload inspection limitations.
Encryption enhances privacy but hinders the effectiveness of conventional security measures.
Need for advanced techniques to detect anomalies in encrypted network communications.

Purpose of the Study:

Introduce ET-SSL, a new method for anomaly detection in encrypted network traffic.
Leverage self-supervised contrastive learning for effective anomaly identification.
Develop a solution that bypasses the need for labeled datasets and payload analysis.

Main Methods:

Utilize self-supervised contrastive learning to extract informative representations from flow-level statistical features.

Keywords:

Anomaly detection Encrypted network traffic Privacy-preserving Self-supervised learning

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Employ packet length, inter-arrival time, flow duration, and protocol metadata for analysis.

Extend SSL-based traffic classification to enhance detection performance with low computational complexity.

Main Results:

Achieved 96.8% accuracy, 92.7% true positive rate (TPR), and 1.2% false positive rate (FPR) on benchmark datasets (CIC-Darknet2020, ISCX VPN, UNSW-NB15).
Demonstrated real-time anomaly detection capabilities with 15-25 ms latency and processing speeds up to 10 Gbps.
Effectively detects zero-day attacks in dynamic network environments.

Conclusions:

ET-SSL provides a paradigm for private and energy-efficient anomaly detection in encrypted traffic.
The method offers superior scalability and effectiveness compared to existing techniques, especially for zero-day threats.
ET-SSL is suitable for high-speed, resource-constrained environments requiring robust security.