International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed
ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)
Webpage URL: https://airccse.org/journal/ijnsa.html
Hybrid Deep Learning Approach for Enhanced Detection and Mitigation of DDOS Attack in SDN Networks
Ahlam Alsufyani, Bashayer Alotaibi and Samah Alajmani, Taif University, Saudi Arabia
Abstract
The pervasiveness of (DDoS) Distributed Denial of Service attacks has intensified the demand for effective and dependable detection methods in Software-Defined Networks (SDNs). This proposed study introduces a hybrid Deep Learning framework designed to identify and address DDoS attacks in Software-Defined Networking (SDN) contexts. Due to the centralization of SDN control planes, these networks are especially susceptible to DDoS attacks, which can saturate system resources and disrupt critical services. Utilizing the CICDDoS2019 dataset, this research integrates Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Adaptive Feature Dimensionality Learning (AFDL) to improve detection accuracy and efficiency. In order to appropriately differentiate between attack and normal traffic, the proposed hybrid model combines both temporal dependencies and feature correlations achieving an accuracy of 99.80%. This research enhances SDN security by offering a scalable and reliable DDoS detection solution capable of adapting to real-time network requirements, addressing the prospective of DL in protecting network infrastructures.
Keywords
Deep Learning, Software Defined Networking (SDN), Distributed Denial of Service (DDoS)
Abstract URL: http://aircconline.com/abstract/ijnsa/v16n6/16624ijnsa05.html
Original Source URL: https://aircconline.com/ijnsa/V16N6/16624ijnsa05.pdf
Volume URL: https://airccse.org/journal/jnsa24_current.html
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