Title
Severity Level Assessment from Semantically Fused Video Content Analysis for Physical Threat Detection in Ground Segments of Space Systems
Abstract
Disaster risks related to natural hazards are evolving gradually, albeit accelerating over time, the human-made and cyber threats are changing rapidly exploiting the increasing progress in technologies and the complex, highly interlinked, modern environment of critical infrastructures. Therefore, as these threats have been intensifying, the actions to strengthen the resilience of critical infrastructures should be step up, by understanding their complex systems as well as the multi-risks nature. In this landscape, the aim of this work focuses to propose a framework enables to identify potential human-made threats, generated by using physical means, and captured by heterogeneous sources (CCTVs, UAVs etc.). Advanced machine learning techniques provide analysis of events and useful information, which are fused semantically and estimate the severity level of the potential attack, serving the needs for real-time monitoring and mitigating the risk.
Year
DOI
Venue
2021
10.1007/978-3-030-95484-0_27
COMPUTER SECURITY: ESORICS 2021 INTERNATIONAL WORKSHOPS
Keywords
DocType
Volume
Risk assessment, Critical infrastructures, Human-made threats, Video-based object detection, Face detection and recognition, Knowledge-based representation, Severity level estimation
Conference
13106
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
9