Abstract
Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic voting-based filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real reports with false votes. Genetic algorithm-based filtering scheme (GAFS) uses a genetic algorithm with a fuzzy rule-based system that considers remaining energy and number of filtered votes in addition to the distance. The analysis results of the current study demonstrate the effectiveness of our scheme against these attacks in comparison with PVFS. The results show increased detection power achieved through effective verification while maintaining energy consumption.
Highlights
Wireless sensor networks (WSNs) have emerged as potential technologies to facilitate wireless communication for a variety of applications [1, 2]
The compromised nodes generate fabricated reports with false votes (FRFV) and reports with false votes (RRFV) attacks using false votes corresponding to the false traffic ratio (FTR) relative to the total number of events
An adversary can seriously harm sensor networks by launching complex attacks such as FRFV and RRFV attacks. Such attacks, which are generated at the application layer, increase unnecessary energy consumption and inhibit the flow of event reporting
Summary
Wireless sensor networks (WSNs) have emerged as potential technologies to facilitate wireless communication for a variety of applications [1, 2]. The BS collects the events information from the sensor nodes for critical decision making. These nodes can be captured and compromised because they are left unattended. The PVFS was proposed to detect FRFV and RRFV attacks in a sensor network. This scheme is suitable for filtering in a cluster-based model. The PVFS has three phases: (1) key initialization and assignment, (2) report generation, and (3) en-route filtering. Report generation and en-route filtering phases are illustrated in Fig. 2 and 3 against the FRFV and RRFV attacks, respectively
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