Anomaly detection in WSN IoT (Internet of Things) environment through a consensus-based anomaly detection approach

The most essential part of any IoT (Internet of Things) model is the wireless network sensors (WSN). The application of these networks combined with the latest technologies relating to IoT provides fast, economical as well as flexible applications. Wireless sensor networks have various applications for IoT where devices combined with the sensors are used for data collection from various environments as well as monitoring of these environments. These networks are highly prone to attacks considering their characteristic nature, which includes self-organization, a topology that is dynamic, large-scale, and constrained on resources. Various models have been proposed for the detection of attacks in these Wireless sensor networks. Although, the recent survey studies on the attacks in this network aims at the methodologies for detecting only one to two kinds of attacks as well as have the absence of performance analysis in detail. This research work proposes a CSAD (consensus-based novel anomaly detection) approach in three steps; first step; each step includes a novel algorithm. A novel distributed algorithm is proposed to classify the anomaly and normal data packets. In the second step level based approach is used for decision implementation to identify the anomaly; also it is responsible for efficient packet transmission. The third step includes discarding the anomaly Moreover, the proposed model is evaluated by inducing the different malicious nodes, and an anomaly detected is observed. Further comparison with the existing model is carried out based on the classified and misclassified packet; through the comparative analysis, it is observed that the Consensus-AD (Anomaly Detection) approach simply outperforms the existing model. A comparative analysis is carried out considering the throughput for model efficiency. Moreover, comparative analysis shows that the proposed model outperforms the existing anomaly detection protocol. The existing model observes a throughput of 80.99% whereas the CSAD model observes a throughput of 81.81%.