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A hybrid machine learning approach for detecting unprecedented DDoS attacks.

Mohammad Najafimehr¹, Sajjad Zarifzadeh¹, Seyedakbar Mostafavi¹

¹Department of Computer Engineering, Yazd University, Yazd, Iran.

The Journal of Supercomputing

|January 12, 2022

Summary

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This study introduces a novel hybrid machine learning method for detecting unknown Distributed Denial of Service (DDoS) attacks. The approach significantly improves detection accuracy compared to traditional methods.

Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

Service availability is critical for computer networks, facing increasing threats from Distributed Denial of Service (DDoS) attacks.
Existing machine learning (ML) methods excel at detecting known DDoS attacks but struggle with novel, unknown malicious traffic patterns.

Purpose of the Study:

To propose and evaluate a novel hybrid machine learning method for enhanced DDoS attack detection, particularly for unknown threats.
To combine unsupervised and supervised learning algorithms to improve the detection of anomalous network traffic.

Main Methods:

A hybrid approach integrating clustering (unsupervised) for anomaly separation and classification (supervised) with statistical measures for cluster labeling.
Utilized a big data processing framework for training on the CICIDS2017 dataset and testing on the CICDDoS2019 dataset.

Keywords:

Big data DBSCAN DDoS detection Machine learning Network security Unprecedented attacks

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Main Results:

The proposed hybrid method demonstrated a significant improvement in detecting unknown malicious traffic.
Achieved a Positive Likelihood Ratio (LR+) approximately 198% higher than conventional ML classification algorithms.

Conclusions:

The novel hybrid machine learning method offers superior performance in identifying unknown DDoS attacks compared to existing ML techniques.
This approach enhances network security by providing more robust detection capabilities against evolving cyber threats.