FFTC: A DDoS attack detection and defense mechanism based on flow feature and threshold control in SDN

Junhan Wang

doi:10.1117/12.3032974

5 July 2024 FFTC: A DDoS attack detection and defense mechanism based on flow feature and threshold control in SDN

Junhan Wang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131847A (2024) https://doi.org/10.1117/12.3032974
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Recent advancement in Software Defined Networking (SDN) has provided great opportunities for general architectural design for future networks. Current SDN is appealing for facilely mastering the separation of control plane and data processing to enhance network control and management. However, the susceptibility of existing SDN networks to malicious distributed denial of service (DDoS) attacks poses serious cybersecurity risks, which potentially results in catastrophic consequences. In this paper, we propose a DDoS attack detection and defense mechanism called FFTC based on flow-based feature extraction and dynamic threshold control to automatically identify adversarial attacks and ensure the security of SDN networks. It aims to analyse the flow behaviour, extract available pattern and adjust threshold to realize accurate identification and real-time response to DDoS attacks, and provide corresponding defense strategies. Experimental results demonstrate that the proposed FFTC mechanism can not only maintain the proper precision and reduce the processing time, but also successfully detect various DDoS attacks and restore the corresponding port communication.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhan Wang "FFTC: A DDoS attack detection and defense mechanism based on flow feature and threshold control in SDN", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131847A (5 July 2024); https://doi.org/10.1117/12.3032974

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Defense and security

Feature extraction

Network security

Machine learning

Floods

Network architectures

Tunable filters

Show All Keywords

Keywords/Phrases

Search In:

Publication Years