Authors:
Van-Khoa Le
;
Edith Grall-Maes
and
Pierre Beauseroy
Affiliation:
Troyes University of Technology, France
Keyword(s):
Anomaly Detection, Statistic Method, Security System, Cyber-physical Attacks, Key Metric.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Health Engineering and Technology Applications
;
Image and Video Analysis
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
This paper presents a detection process which utilizes various sensors (camera, card readers, movement detector)
for detecting automatically abnormal events. The detection process strengthens current security systems to
identify attackers in the context of building and office. Key metrics are proposed to describe people’s behavior
in critical zones of the building. They are built using measures from the sensors, which provide information
about the person, the position, and the instant. These metrics are used to classify abnormal behaviors from
regular ones, based on a statistical classifier. This technique is tested on both simulated data and real data,
in which an attacking scenario was prepared by security experts. Results show that abnormal events from
the scenario have been successfully detected. The experiments demonstrate that the proposed key metrics are
relevant and the proposed detection scheme is appropriate for infrastructure surveillance.