Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure

Sabeur, Zoheir; Zlatev, Zlatko; Melas, Panagiotis; Veres, Galina; Arbab-Zavar, Banafshe; Middleton, Lee J; Museux, Nicolas

doi:10.1007/978-3-319-89935-0_20

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…Nevertheless, in this subsection, comparison of the proposed to three existing schemes is provided. In particular: (a) the complexity of the proposed scheme is compared to the complexity of a linear scheme [69], where the concept of omni-directional propagation of the blast wave is presented, (b) the threat notification time of the proposed scheme is compared to the one presented in [70], where sensors/cameras are mounted along the perimeter fence of a CI and (c) the estimated surveillance equipment cost of the proposed scheme is compared to the cost of [71], where surveillance of the area of a CI is accomplished by a grid of sensors.…”

Section: Comparison To Other Approachesmentioning

confidence: 99%

“…This time is enough for trained forces to stop a possible attack. On the other hand, in [70] sensors/cameras are mounted along the perimeter fence of a CI. Even if a truck can be detected and its size can be estimated at every point of the field of view of a fence-mounted sensor/camera, in several cases the structure and morphology of the roads do not allow large radiuses of surveillance, for the specific settings and for the performed experiments.…”

Section: Figure 12mentioning

confidence: 99%

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Real Time Threat Assessment of Truck Cargos Carrying Dangerous Goods for Preventing Terrorism Attacks on Neighboring Critical Infrastructures

2022

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Critical infrastructures are assets of invaluable importance, essential for the whole world. Since they serve core functions of our societies, they often become targets of terrorists. Many critical infrastructures are vulnerable, due to their short distance from public roads and in the past years, several vehicle-bomb incidents have been recorded. This paper focuses on the case of truck-bombs, which can either be created from scratch, or terrorists can easily hijack truck cargos carrying dangerous goods and turn them into bombs. The latter are typically called ADR truck cargos, according to the respective agreement of the 30th of September 1957, concerning the international carriage of dangerous goods by road. The proposed scheme performs threat assessment of neighboring critical infrastructures, aiming at preventing explosions of truck-bombs. To do so, each crucial point of a critical infrastructure is initially associated with a level of importance. Next, three scenarios are analyzed: (a) single-attack single-infrastructure, (b) multiple-attack single-infrastructure, and (c) multiple-attack multiple-infrastructure. To reduce computational complexity, the third scenario is simplified to one of the two other scenarios, by introducing a novel fusion technique for the non-overlapping segments of the Voronoi tessellation. By this way, an area of threat assessment is estimated for each critical infrastructure. Then, the threat level is assessed in real time by an innovative algorithm, which: (a) estimates the impact of multiple consecutive explosions, (b) uses five adapted threat levels and (c) introduces multiple criteria and minimum classification conditions based on the number of crucial points and their levels of importance. Extensive real world experimental results and comparisons to other works, exhibit the pros and cons of the proposed scheme. In particular the proposed scheme improves: (a) computational time by 74.5%, compared to [69], (b) threat notification time by 86.9% compared to [70] and (c) estimated surveillance cost by 98.6% compared to [71].

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Section: Comparison To Other Approachesmentioning

confidence: 99%

Section: Figure 12mentioning

confidence: 99%

Real Time Threat Assessment of Truck Cargos Carrying Dangerous Goods for Preventing Terrorism Attacks on Neighboring Critical Infrastructures

2022

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“…[8]. With the recent advancement in IoT, large streams of big data can be generated and from which critical knowledge can be extracted in real-time using machines reasoning in order to understand threats and/or attacks on urban spaces with high context awareness [9], [10]. The MAIDS twin of S4AllCities SoS, adopts the JDL data fusion framework, for big data processing and analytics while it can efficiently organize them at various fusion levels, which themselves are levels of cyber-physical situational awareness.…”

Section: Data Fusion Modelling and Reasoningmentioning

confidence: 99%

Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces

Sabeur

Angelopoulos

Collick

et al. 2021

Advances in Neuroergonomics and Cognitive Engineering

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The ever-growing adoption of big data technologies, smart sensing, data science and artificial intelligence is enabling the development of new intelligent urban spaces with real-time monitoring and advanced cyber-physical situational awareness capabilities. In the S4AllCities international research project, the advancement of cyber-physical situational awareness will be experimented for achieving safer smart city spaces in Europe and beyond. The deployment of digital twins will lead to understanding real-time situation awareness and risks of potential physical and/or cyber-attacks on urban critical infrastructure specifically. The critical extraction of knowledge using digital twins, which ingest, process and fuse observation data and information, prior to machine reasoning is performed in S4AllCities. In this paper, a cyber behavior detection module, which identifies unusualness in cyber traffic networks is described. Also, a physical behaviour detection module is introduced. The two modules function within the so-called Malicious Attacks Information Detection System (MAIDS) digital twin.

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“…The Joint Directorate of Laboratories model (JDL) is a common framework for enabling situational awareness for decision support contexts. Initially developed for military systems, its use in large-scale critical infrastructure monitoring has been an effective framework to drive a structured and scalable information fusion system that incorporates the user with machine-driven analytics [27]. With respect to cyber defence, a lack of literature has been delivered applying the JDL framework.…”

Section: Jdl Modelmentioning

confidence: 99%

Towards a Multi-Layered Phishing Detection

Rendall

Nisioti

Mylonas

2020

Sensors

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Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

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Large Scale Surveillance, Detection and Alerts Information Management System for Critical Infrastructure

Cited by 7 publications

References 12 publications

Real Time Threat Assessment of Truck Cargos Carrying Dangerous Goods for Preventing Terrorism Attacks on Neighboring Critical Infrastructures

Real Time Threat Assessment of Truck Cargos Carrying Dangerous Goods for Preventing Terrorism Attacks on Neighboring Critical Infrastructures

Advanced Cyber and Physical Situation Awareness in Urban Smart Spaces

Towards a Multi-Layered Phishing Detection

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