Assessment of Conflict Flows in Software-Defined Networks using a Novel Nature-Inspired Optimization-Tuned Kernelized SVM


  • Amit Sharma, Veena M., Hirald Dwaraka Praveena, V. Selvakumar, Bhuvana J., Dhiraj Singh


Conflict Flow, Security, Software-Defined Network (SDN), Tree-Seed Optimization-Tuned Kernelized Support Vector Machine (TSO-KSVM)


The centralizing management and flexibly customizing network resources, Software-Defined Networks (SDN) completely transform network administration. As networks become more intricate, the likelihood of conflicts arising data flows increases, potentially leading to a decline in overall performance and the emergence of security vulnerabilities. This paper presents a tree-seed optimization-tuned kernelized support vector machine (TSO-KSVM) for the assessment of conflict flows in SDN environments. Initially, we gather data samples of SDN in conflict flows to analyze the performance of the proposed method. Applying the min-max scaling method to preprocess the raw data samples and linear discriminant analysis (LDA) is carried out to reduce the dimension. In the proposed framework, TSO is applied to enhance the assessment in the KSVM model. The proposed method is implemented in the Python tool. The proposed method's performance is analyzed in terms of various metrics compared with other methods. From the experimented results, we conclude that the proposed method attains the greatest accuracy rate of other methods in assessing conflict flows in SDN networks.


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How to Cite

Amit Sharma, Veena M., Hirald Dwaraka Praveena, V. Selvakumar, Bhuvana J., Dhiraj Singh, . (2024). Assessment of Conflict Flows in Software-Defined Networks using a Novel Nature-Inspired Optimization-Tuned Kernelized SVM. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1040–1044. Retrieved from



Research Article