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Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN.

Xingyu Gong¹, Ke Cao¹, Na Li¹

¹College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Computational Intelligence and Neuroscience

|June 3, 2022

Summary

This summary is machine-generated.

This study introduces a novel network abnormal traffic detection algorithm, RIC-SC-DeCN, which enhances accuracy and reduces training time. The method effectively addresses data redundancy and information loss in network traffic analysis.

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Area of Science:

Computer Science
Cybersecurity
Network Engineering

Background:

Network traffic data often suffers from high dimensionality and redundancy.
Convolutional Neural Networks (CNNs) can lose crucial information during pooling operations, impacting detection accuracy.
Existing methods for network abnormal traffic detection face challenges in effectively handling complex traffic data characteristics.

Purpose of the Study:

To propose a novel network abnormal traffic detection algorithm, RIC-SC-DeCN, to overcome limitations of existing methods.
To reduce data redundancy and information loss inherent in network traffic analysis.
To improve the overall effectiveness and efficiency of network abnormal traffic detection.

Main Methods:

Recursive Information Correlation (RIC) feature selection mechanism: Employs maximum information correlation and recursive feature elimination to reduce data redundancy.
Skip-Connected Deconvolutional Neural Network (SC-DeCN) model: Reconstructs input signals to minimize information loss.
Integration of RIC and SC-DeCN: Forms the proposed RIC-SC-DeCN algorithm for comprehensive detection.

Main Results:

The RIC feature selection mechanism achieved 96.22% accuracy with the MSCNN detection model.
The SC-DeCN model demonstrated high detection accuracy (96.55%) with moderate training time.
The integrated RIC-SC-DeCN algorithm improved accuracy to 97.68% and reduced overall training time by 45.50% compared to SC-DeCN.

Conclusions:

The proposed RIC-SC-DeCN algorithm significantly enhances network abnormal traffic detection accuracy.
The algorithm effectively mitigates issues of data redundancy and information loss.
RIC-SC-DeCN offers an efficient and effective solution for detecting abnormal network traffic.