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Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior.

Man Li¹, Huachun Zhou¹, Yajuan Qin¹

¹School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)

|April 12, 2022

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This study introduces a two-stage intelligent detection model for 5G network security. The model effectively identifies malicious traffic and classifies DDoS attacks, enhancing 5G security against evolving threats.

Area of Science:

Network Security
Telecommunications Engineering
Artificial Intelligence

Background:

5G technologies offer widespread connectivity but face significant security challenges.
Existing public datasets for network security analysis are often outdated, necessitating new data generation methods.
The increasing complexity of cyber threats requires advanced detection mechanisms for 5G networks.

Purpose of the Study:

To propose a novel two-stage intelligent detection model for enhancing 5G network security.
To address the limitations of outdated public datasets by creating a self-generated dataset.
To define and apply metrics for evaluating malicious traffic detection capabilities.

Main Methods:

Development of a two-stage intelligent detection model tailored for 5G environments.

Keywords:

DDoS malicious behavior neural network model statistic model

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Generation of a custom dataset using a virtual platform to simulate realistic network traffic.

Definition of specific metrics to quantify the capability of detecting malicious traffic.

Comparative analysis of the proposed model against benchmark algorithms and neural networks.

Main Results:

The proposed model accurately distinguishes between benign and abnormal network traffic.
The model successfully classifies 21 different types of Distributed Denial of Service (DDoS) attacks.
Experimental results demonstrate superior performance compared to existing methods in detection rate and response time.

Conclusions:

The two-stage intelligent detection model provides a robust solution for 5G network security.
The self-generated dataset and defined metrics offer a reliable framework for evaluating security mechanisms.
The proposed approach significantly improves the detection of malicious traffic and DDoS attacks in 5G networks.