Mathematical Models of Nonlinear Phenomena in Wireless Security

The proliferation of malicious components seriously threatens network integrity and performance in wireless security. Conventional methods of solving this problem might not be able to adequately depict the complexity of nonlinear interactions and dynamics. This paper offers a new mathematical model combining nonlinear fractional objective functions inside artificial neural networks (ANNs), hence improving the detection and control of malicious node propagation. Background research indicates that conventional approaches struggle to handle the irregular nature of hostile behavior and its consequences on network stability. We present a deep ANN framework to integrate fractional calculus, which predicts the nonlinear dynamics of malicious node spread, so improving detection accuracy and reaction strategies. The fractional objective function enables the model to better adapt to real-time network changes by considering the complicated, time-dependent character of attack propagation. Our approach seems to be successful experimentally; false positives are reduced and detection rates are considerably raised. Our strategy especially improved detection accuracy by 20% above baseline methods and lowered false positive rates by 15%. These results show how effectively nonlinear fractional models could enhance security of wireless networks. In addressing the issues generated by hostile nodes in dynamic wireless systems, the suggested method represents a significant development.