EyeSim: A mobile application for visual-assisted wormhole attack detection in IoT-enabled WSNs

Internet of Things (IoT) have emerged as a valuable, flexible, and interoperable network of devices, objects, items, and electronics. Fuelled by recent advances in networking, communications, computation, software, and hardware technologies, IoT has stepped out of its infancy and is considered as the next breakthrough technology in transforming the Internet into a fully integrated Future Internet. Wireless Sensor Networks (WSNs) are utilized by IoT to collect, exchange, and deliver data remotely leveraging the potential of IoT in practical applications and services. However, delivering data remotely might be threatened by various and serious security attacks. This work focuses on developing a visual-assisted tool for exposing security threats in IP-enabled WSNs. The proposed tool, called EyeSim, is a human-interactive visual-based anomaly detection system that is capable of monitoring and promptly alerting for the presence of wormhole links. In addition, it is capable of indicating the malicious nodes that form the wormhole link. EyeSim may expose adversaries by conducting cognitive network data analysis based on dynamic routing information. The efficacy of EyeSim is assessed in terms of detection accuracy. The simulation results show that EyeSim has the capabilities to accurately detect multiple wormhole attacks in real-time.