ENHANCING SECURITY AND ENERGY EFFICIENCY IN SDN NETWORKS THROUGH MACHINE LEARNING-ASSISTED TRUST SECURE ATTACKER
SDN networks become more vulnerable to assaults as they grow in size, necessitating the use of strong security methods. Due to the sensor nodes' limited energy, compute capabilities, and storage resources, identifying adequate cryptography for wireless sensor networks is a serious problem. For Adhoc networks, new energy-aware routing algorithms called trustworthy Machine Learning Trust Secure Attacker Detection will be provided. Energy efficiency, reliability, data aggregation, and attacker detection are all essential SDN criteria addressed by MLTSAD. MLTSAD is an energy-efficient routing method that creates routes for end-to-end packet traversal that utilize the least amount of energy while simultaneously increasing malicious node detection. We presented a cryptography-based security method to deploy the encryption approach in SDN. Improving the encryption and decryption features of an existing method, allowing for high security. In a series of studies, we examined the MLTSAD and DLTSAD algorithms in order to increase network performance in terms of metrics like energy consumption, packet delivery ratio, latency, and network longevity.
SDN networks, Security, MLTSAD, DLTSAD, Latency, Network reliability