An efficient intrusion detection framework for mitigating blackhole and sinkhole attacks in healthcare wireless sensor networks

A Wireless Sensor Network (WSN) is made up of physically dispersed autonomous sensors which monitor the network and gather data about its surroundings. A sensor effectively captures information securely with the goal to detect potentially dangerous user conduct in the network. However, identifying an attack from ordinary nodes of sensors remains a difficult task. As a result, the suggested efforts concentrate on building effective attack discovery methods for secured data packet broadcasting from source to destination in the WSN. Proportional Overlapping ScoreBased Minkowski K-Means Clustering (POS-MKC) is proposed to improve attack detection accuracy with lesser computational complexity in WSN healthcare applications. The simulation results illustrate that the proposed MK-Means framework widely generates an optimized performance with the reduction of delay, computational complexity and improvements in packet delivery ratio with high attack detection accuracy as compared to the state-of-the-art works.

Automated summary

A wireless sensor network is composed of dispersed sensors that monitor and collect data. However, identifying attacks from ordinary nodes remains difficult. This study proposes an improved attack detection method with higher accuracy and lower computational complexity.