Modeling DDOS attacks in sdn and detection using random forest classifier

Abdullahi Wabi, Aishatu; Idris, Ismail; Mikail Olaniyi, Olayemi; Joseph A.,; Surajudeen Adebayo, Olawale

doi:10.1080/23742917.2023.2264435

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Cited by 2 publications

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A Ddos Attack Detection Framework: Leveraging Feature Selection Integration and Random Forest Optimization for Improved Security

Chong,

Emad Ali,

Manickam

et al. 2023

Preprint

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A Ddos Attack Detection Framework: Leveraging Feature Selection Integration and Random Forest Optimization for Improved Security

Chong,

Emad Ali,

Manickam

et al. 2023

Preprint

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A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

Su,

Xiong,

Qian

et al. 2024

Electronics

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The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions.

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Modeling DDOS attacks in sdn and detection using random forest classifier

Cited by 2 publications

References 21 publications

A Ddos Attack Detection Framework: Leveraging Feature Selection Integration and Random Forest Optimization for Improved Security

A Ddos Attack Detection Framework: Leveraging Feature Selection Integration and Random Forest Optimization for Improved Security

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

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