WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to defend against false report attacks in extreme attack circumstances. As attacks have recently become more diverse and more intelligent, WSNs require intelligent methods of security. Based on the report information gathered from the base station, the proposed method provides a technique to prevent attacks that may occur where all MAC information is damaged by carrying out verification of a false report attack through the machine learning based prediction model and the evaluation function.

Today, wireless sensor networks (WSNs) are applied to various industries such as building automation, medical, security, intelligent agriculture, and disaster monitoring. A WSN consists of hundreds to thousands of tiny sensor nodes that perform monitoring tasks. A small sensor node has a limited amount of internal memory and energy resources. Sensor nodes are used to detect a variety of data in specific environmental areas. As a result, WSN should be energy efficient. Sensor nodes are vulnerable to false report injection attacks because they are deployed in an open environment. A false report injection attack consumes the limited energy of a node more quickly and confuses the user. CFFS has been proposed to prevent such an attack using a method of en-route filtering false reports by dividing nodes into clusters. However, the CFFS scheme is vulnerable for repeated false report injection attacks. In this paper, we propose an approach to prolong the WSN lifetime by adjusting the dynamic security threshold value and using a fuzzy logic-based key redistribution selection of cluster head nodes. The proposed method increases the detection rate for repeated false report injection attacks by adding the additional key distribution phase in the existing method. The experimental results show that the energy efficiency of the proposed method was increased by 40.278%.