Abdul Majid
Wireless networks are increasingly exposed to cybercrime because of open communication channels, distributed nodes, and evolving attack strategies. Traditional cybersecurity methods may not fully capture the dynamic spread of cyber threats across connected systems. This study develops a biomathematical model inspired by biological infection and immune-response mechanisms to analyze cybercrime detection and prevention in secure wireless networks. The proposed model classifies wireless network nodes into susceptible, infected, protected, and recovered compartments. Cyberattack transmission, protection, detection, recovery, and loss of security immunity are represented using ordinary differential equations. Equilibrium analysis, stability conditions, the cybercrime reproduction number R_0, numerical and sensitivity analysis are used to evaluate attack control and network resilience. The model shows that cybercrime declines when R_0<1, indicating that detection and recovery rates exceed the attack transmission rate. Numerical results further show that adaptive biomathematical defense reduces infected nodes, improves detection performance, and increases network resilience compared with non-adaptive defense. The proposed framework provides a mathematical basis for cybercrime detection and prevention in wireless networks and supports adaptive security planning through improved detection, rapid recovery, and strengthened protection mechanisms.