Work

Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks

Year: 2024

Type: article

Abstract: As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security architecture for anomaly detection. Subsequently, a neighborhood information system is established to ascertain the relationships between data objects with mixed attributes. We then develop the WNIN to encapsulate the relationships, and a state-transferring probability matrix based on data object similarity is derived. Ultimately, a random wandering process within the WNIN is executed, and the importance of data objects is evaluated using the steady-state distribution vector, thereby determining the anomaly data. Simulation outcomes reveal that our proposed method attains superior anomaly detection rates compared with existing methods.

Source: Electronics

DOI: 10.3390/electronics13173482

Authors: Chunyan An, Yingyi Liu, Qi Li, Pengbo Si

Institutions: State Grid Corporation of China (China), China Electric Power Research Institute, Beihang University, Beijing University of Technology

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: State Grid Corporation of China