Treffer: Securing Software-Defined Networks with Multi-Layer Defense Mechanisms.

Today's computer networks have ever-increasing data traffic, making massive amounts of network administration difficult in terms of maintaining service quality. The method for designing networks is a software-defined network (SDN) that harmonizes the settings of each networking device into a single programmed essential administrator, making it possible to program or manage the network effectively and dynamically. In this study, we proposed a novel Kookaburra-Optimized Multilayer Recurrent Neural Network (KO-MRNN) for detecting attacks over the SDN. Security flaws in this design can lead to attacks like port scans and distributed denial of service (DDoS). Thus, security measures are required to ensure that the central controller of SDN operates normally. Python is used to implement the suggested approach. Results show that the proposed method performed better in terms of average false alarm rate, average detection rate, precision and accuracy. As a result of testing the proposed method using generated IP traffic data, we were able to achieve positive results for both detection and mitigation. [ABSTRACT FROM AUTHOR]

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