Hybrid Deep Learning Model for Real-Time Detection of Distributed Denial of Service Attacks in Software Defined Networks

Makuvaza, Auther; Jat, Dharm Singh; Gamundani, Attlee M.

doi:10.1007/978-981-16-3915-9_1

Cited by 1 publication

(3 citation statements)

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“…Another study [14] supported [10] by revealing that combining several deep learning algorithms such as RF, CNN, and MLP methods improved DDoS attack detection in IoT networks and devices. In the research, Ref.…”

Section: Hybrid Deep Learning Techniquesmentioning

confidence: 98%

“…Some studies have utilized single algorithms, such as convolutional neural networks (CNNs) or long short-term memory (LSTM) approaches, while others have employed hybrid algorithms combining multiple techniques. According to [9,10], hybrid deep learning algorithms are the most widely used approaches for detecting and mitigating attack traffic within SDNs. In this section, we will discuss different approaches to demonstrate the significance of deep learning in the context of securing SDNs against DDoS attacks.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the analysis also indicated that deep learning algorithms could better detect DDoS attacks in SDN environments [6][7][8][9][10][11][12][13][14][15][16][17][18]. The research emerged that deep learning techniques outperformed classical machine learning algorithms, as they did not involve human interaction to improve their performance but instead relied on artificial neural networks [24].…”

Section: Hybrid Deep Learning Techniquesmentioning

confidence: 99%

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Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Elubeyd

Yiltas-Kaplan

2023

Applied Sciences

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This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

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Section: Hybrid Deep Learning Techniquesmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%