Dynamic adaptation of scan rates for efficient and congestion-aware internet-wide scanning in IoT security

Nowadays, internet-wide scanning is considered as a countermeasure against the security problems in internet-of-things (IoT). The network congestion problem severely affects the IoT system due to the presenting number of port scanning packets in the network. The network congestion is taken as a major issue so the research paper is proposed for efficient internet-wide scanning in wireless LANs by minimizing network congestion. Adaptive Particle Network Optimization (APNO) is proposed in this paper that adaptively adjusts scan rates based on real-time network conditions and traffic dynamics. To enhance the adaptability and search efficiency, improvements are provided in the initialization of particle positions, velocity computation, and convergence mechanisms. The adaptive approach is implemented to ensure efficient scanning while minimizing network congestion during IoT data communication. The scan rate is adjusted based on the threshold values by defining network congestion limits and IoT data throughput requirements. The simulations are conducted on an NS-3 simulator that covers a wide range of scenarios, including varying scan rate ranges, traffic loads, network densities, and threshold sensitivities. The comparative analysis displayed a better outcome from the proposed APNO algorithm and achieved a scan efficiency of 98.2%, congestion level of 2.1% and IoT data throughput of 240Mbps. The convergence result showed a significant improvement in Internet-wide scanning in IoT environments.