Risk-Based Methodology for Real-Time Security Monitoring of Interdependent Services in Critical Infrastructures

2010 ◽  
Author(s):  
Jocelyn Aubert ◽  
Thomas Schaberreiter ◽  
Christophe Incoul ◽  
Djamel Khadraoui ◽  
Benjamin Gâteau
Keyword(s):  
Real Time ◽  
Critical Infrastructures ◽  
Security Monitoring

Dynamic recovery of critical infrastructures: real-time temporal coordination

2008 ◽  
Vol 4 (1/2) ◽  
pp. 17 ◽  
Author(s):  
Jose R. Marti ◽  
Jorge A. Hollman ◽  
Carlos Ventura ◽  
Juri Jatskevich
Keyword(s):  
Real Time ◽  
Dynamic Recovery ◽  
Critical Infrastructures ◽  
Temporal Coordination

Near-miss situation based visual analysis of SIEM rules for real time network security monitoring

2018 ◽  
Vol 10 (4) ◽  
pp. 1509-1526 ◽  
Author(s):  
Abdul Majeed ◽  
Raihan ur Rasool ◽  
Farooq Ahmad ◽  
Masoom Alam ◽  
Nadeem Javaid
Keyword(s):  
Network Security ◽  
Real Time ◽  
Visual Analysis ◽  
Near Miss ◽  
Security Monitoring

Application of speed-enhanced spatial domain correlation filters for real-time security monitoring

2011 ◽  
Author(s):  
Akber Gardezi ◽  
Nagachetan Bangalore ◽  
Ahmed Al-Kandri ◽  
Philip Birch ◽  
Rupert Young ◽  
...  
Keyword(s):  
Real Time ◽  
Spatial Domain ◽  
Correlation Filters ◽  
Security Monitoring

A Study of Real Time Security Monitoring of Commercial Vehicles Using a Microscopic Traffic Simulation Model

2011 ◽  
Vol 55-57 ◽  
pp. 1293-1298
Author(s):  
Hao Wang ◽  
Ruey Cheu ◽  
Der Horng Lee
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Detection Algorithm ◽  
Commercial Vehicles ◽  
Microscopic Simulation ◽  
Real Time System ◽  
Security Monitoring ◽  
Positioning Systems ◽  
Good Potential ◽  
Security Monitoring System

This paper involves a study of a real-time system for monitoring the security of commercial vehicles in road networks. Embedded in the security monitoring system is a commercial vehicle tracking and incident detection algorithm which relies on a combination of vehicle telemetry data obtained from Global Positioning Systems and on-board sensors to continuously monitor the route choice and car-following behavior of the driver. The performance of this algorithm has been tested in a microscopic simulation model, on a set of hypothetical scenarios, which included deviations from the approved routes, forced to travel at unreasonably low speeds, or even stopped at unexpected places in the network. The initial results indicate that the proposed system has good potential in detecting abnormal driving behaviors, with 100% detection rate, 6.0 seconds of mean detection time, and less than 1% false alarm rate.

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