The concept of Software Defined Networking (SDN) represents a modern approach
to networking that separates the control plane from the data plane through
network abstraction, resulting in a flexible, programmable and dynamic
architecture compared to traditional networks. The separation of control and
data planes has led to a high degree of network resilience, but has also given
rise to new security risks, including the threat of distributed
denial-of-service (DDoS) attacks, which pose a new challenge in the SDN
environment. In this paper, the effectiveness of using machine learning
algorithms to detect distributed denial-of-service (DDoS) attacks in
software-defined networking (SDN) environments is investigated. Four
algorithms, including Random Forest, Decision Tree, Support Vector Machine, and
XGBoost, were tested on the CICDDoS2019 dataset, with the timestamp feature
dropped among others. Performance was assessed by measures of accuracy, recall,
accuracy, and F1 score, with the Random Forest algorithm having the highest
accuracy, at 68.9%. The results indicate that ML-based detection is a more
accurate and effective method for identifying DDoS attacks in SDN, despite the
computational requirements of non-parametric algorithms.

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Author Of this post: <a href="">Ahmad Hamarshe</a>, <a href="">Huthaifa I. Ashqar</a>, <a href="">Mohammad Hamarsheh</a>

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