Wireless Sensor Network Modeling and Analysis for Attack Detection

Wireless Sensor Networks (WSN) have gained significant attention over recent years due to their extensive applications in various domains such as environmental monitoring, healthcare systems, industrial automation, and smart cities. However, such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities. The paper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks. Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user errors or malicious activities. A proposed integrated approach for data collection, preprocessing, and analysis in WSN outlines a series of steps applicable throughout both the design phase and operation stage. This ensures effective detection of attacks and anomalies within WSNs. An introduced attack model specifies potential types of unauthorized network layer attacks targeting network nodes, transmitted data, and services offered by the WSN. Furthermore, a graph-based analytical framework was designed to detect attacks by evaluating real-time events from network nodes and determining if an attack is underway. Additionally, a simulation model based on sequences of imperative rules defining behaviors of both regular and compromised nodes is presented. Overall, this technique was experimentally verified using a segment of a WSN embedded in a smart city infrastructure, simulating a wormhole attack. Results demonstrate the viability and practical significance of the technique for enhancing future information security measures. Validation tests confirmed high levels of accuracy and efficiency when applied specifically to detecting wormhole attacks targeting routing protocols in WSNs. Precision and recall rates averaged above the benchmark value of 0.95, thus validating the broad applicability of the proposed models across varied scenarios.