Development of Hybrid Cognitive Security Managers on Improved Multilayer CFA Feed Forward Neural Network to Improve the Security on Wireless Networks

Wireless Local Area Networks (WLANs) that are currently deployed are vulnerable to assaults. The wireless networks have been secured with the application of cognition. A variety of soft computing methods can be utilized to accomplish cognition, whereby they are employed to supply the intelligence required to comprehend user nodes' malevolent actions. The consumer nodes activities are inherently dynamic. The strategies for autonomous protection and adaptation of the Wireless Network environment are investigated as a result of basic weaknesses within the IEEE 802.11 Access Control (AC) mechanism. In the cognitive framework architecture, an Improved Multilayer CFA (Color Filter Array) Feed Forward Neural Network (IMCFFNN) proposed to generate node behaviour patterns and then to analyze them using supervised multilayer CFA feedforward neural networks. The Cognitive Security Managers (CSM) can attain Cognition because the multilayer neural networks are efficient at detecting variations in user node behaviors. The CFA uses the Physical Architecture description layer (PADL) to identify nodes. A solid and effective framework has been developed from this work. Therefore, experiments have proved that malicious node behavior can be detected with 99 % effectiveness. In comparison to unsupervised teaching methods, similar rates of 94% are seen in the laboratory.