Advanced Mitigation of Network Attack in IoT-SDN Using Logistic Model Tree Based Stacked LSTM

Increase of the Internet of Things (IoT) and the adoption of Software-Defined Networking (SDN) developments offer higher scalability and control. Network administration is hence considered a dynamic with SDN, while network control and data flow are kept distinct. In this paper, a novel approach is developed to enhance network security in the Internet of Things (loT)-Software Defined Networking (SDN) environment. The research proposes a Logistic Model Tree based Stacked LSTM model (LMT-SLSTM). The proposed LMT-SLSTM with Logistic Model improves the security. Stacked LSTM networks are used to maintain temporal dependencies in sequential input. From the results of simulation, the network traffic analysis is studied, and the unusual behaviors are identified. The proposed LMT-SLSTM method achieves an accuracy of 99.5% better than the existing methods. The scalability and effectiveness of the LMT-SLSTM framework, in larger and more intricate network environments can be seen in network security challenges.