A Trust Secure Attacker Detection with Upgraded Deep Learning-Assistance for SDN Networks

Software Defined Networking (SDN) technologies may be used to properly identify and monitor network security vulnerabilities brought about by the emergence of programmable features. SDN is a real possibility for modernizing internet network architecture. Due to its centralized nature, SDN architecture increases the frequency of assaults. Providing safety for the SDN is crucial. SDN has made it possible to improve the efficiency and adaptability of network security. In this study, we propose a system for security, energy savings, and intrusion detection based on Deep Learning Trust Secure Attacker Detection (IDLTSAD). There are a lot of benefits to using IDLTSAD, the most notable of which are increased network longevity and safety. Our cryptographic security solution was designed to be used in conjunction with SDN's encryption approach. Our approach is safe since we introduced an attribute-based encryption (ABE) mechanism. We compared the MLTSAD, DLTSAD, or EMLTSAD models to our IDLTSAD technique and ran a battery of experiments to see how each one fared in terms of energy efficiency, packet delivery rate, latency, and overall network longevity.