Security of Next-Generation Networks: A Hybrid Approach Using ML-Algorithm and Game Theory with SDWSN

Abstract

The world is moving towards an internet revolution as technology is improving day by day and usage of the internet is increasing as well. With the increase in usage of the internet and improved technology, we are facing new more vulnerable and sophisticated attacks. To deal with them we have secure our network and make sure we maximize the security and minimize the threat to our technology and network. In this paper, we have analyzed the network security models, comparatively. SDWSN (Software Defined Wireless Sensor Network) is one of the most secure and advanced networking that uses the EASDN (Energy Aware Software Defined Network) protocol that maximizes the efficiency of the network. We have proposed that if we integrate XGBOOST or Random Forest ML learning to train data for DDoS detection and Game theory for network security we can build a more secure and efficient network model. In the future, we are focusing on how can we integrate ZTNA (Zero Trust Network Architecture) in the proposed model.