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Deep learning approaches for detecting DDoS attacks: a systematic review.

Meenakshi Mittal¹, Krishan Kumar¹, Sunny Behal²

¹UIET: University Institute of Engineering and Technology, Chandigarh, India.

|February 1, 2022

Summary

This summary is machine-generated.

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This systematic literature review explores deep learning for detecting Distributed Denial of Service (DDoS) attacks. It highlights current approaches, datasets, and identifies research gaps for enhanced cybersecurity defenses.

Area of Science:

Cybersecurity
Machine Learning
Network Security

Background:

The shift to online platforms, accelerated by the COVID-19 pandemic, has led to a surge in cyberattacks.
Distributed Denial of Service (DDoS) attacks pose a significant threat, capable of disrupting internet-based services.
Traditional DDoS detection methods struggle against evolving attacker strategies and increasing data volumes.

Purpose of the Study:

To systematically review and analyze deep learning approaches for detecting DDoS attacks.
To identify current methodologies, strengths, weaknesses, and benchmark datasets used in the literature.
To pinpoint research gaps and suggest future research directions in deep learning-based DDoS detection.

Main Methods:

A systematic literature review (SLR) was conducted.

Keywords:

Datasets Deep learning Distributed Denial of Service attacks Performance metrics

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Searches were performed across major digital libraries (IEEE, ACM, ScienceDirect, Springer) and Google Scholar.

Relevant studies were analyzed and categorized into key research areas.

Main Results:

Categorization of various deep learning techniques for DDoS detection.
Analysis of methodologies, strengths, and limitations of existing deep learning models.
Overview of benchmark datasets, attack classes, preprocessing strategies, and performance metrics.

Conclusions:

Deep learning shows promise for advanced DDoS attack detection.
Further research is needed to address identified gaps in current deep learning approaches.
Standardization of datasets and methodologies could improve comparative analysis and model development.