A Bayesian network to manage risks of maritime piracy against offshore oil fields

Bouejla, Amal; Chaze, Xavier; Guarnieri, Franck; Napoli, Aldo

doi:10.1016/j.ssci.2014.04.010

Cited by 75 publications

(38 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The vast majority of anomaly detection techniques used in maritime domain is data-driven. Considering the machine learning methods, the Bayesian network seems to be particularly useful in detection of single-point vessel anomalies (such as location, course, speed) [9,11,22,23], as well as to detect anomalies in the whole scenario of cooperation between a ship and pilot-boat [8] or piracy on oil platforms [4]. Neural networks are another popular classification-based technique, used to detect anomalies in ship's speed, position, and course [3,28].…”

Section: Related Workmentioning

confidence: 99%

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Filipiak

Stróżyna

Węcel

et al. 2018

Zeszyty Naukowe Akademii Marynarki Wojennej

View full text Add to dashboard Cite

The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies analysed have been grouped into: traffic analysis, static anomalies, and loitering detection. The analysis was carried out on data describing movement of tankers worldwide in 2015, using sophisticated algorithms and technology capable of handling big data in a fast and efficient manner. The research was conducted as a follow-up of the EDA-funded SIMMO project, which resulted in a maritime surveillance system based on AIS messages enriched with data acquired from open Internet sources.

show abstract

Section: Related Workmentioning

confidence: 99%

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Filipiak

Stróżyna

Węcel

et al. 2018

Zeszyty Naukowe Akademii Marynarki Wojennej

View full text Add to dashboard Cite

show abstract

“…In [7], the potentials of a Bayesian network for effective identification of threats due to piracy to oil industry infrastructure was investigated. They identified the inadequacies of existing systems in handling multi-parameter conditions with a high degree of state variability -which are a more frequent feature such environments.…”

Section: Related Workmentioning

confidence: 99%

Oil and Gas Pipeline Monitoring using Artificial Neural Network

Nwiabu¹,

Igbudu²

2017

IJCA

View full text Add to dashboard Cite

Conventional Artificial Neural Network approaches such as Feed-Forward Networks has been used in diverse applications but are not naturally predictive and also require supervised learning. Feed-forward Artificial Neural Network also trained by backpropagation poses the problem of varnishing gradient. Long Short Term Memory is an Artificial Neural Network recurrent technique that allows long range contextual representation to be stored and learnt in an unsupervised manner. In this work a modified Sparse Distributed LSTM Algorithm using Gaussian membership function with a context-decision gate for detection and monitoring operations has been proposed as an alternative to the traditional FeedForward Architecture. The AI monitoring System shows promising results in solving many recurrent problems, particularly those requiring long-term storage dependenciesthe Vanishing Gradient problem (VGP) and has the ability to use contextual information when mapping between input and output sequences. The Oil and Gas AI monitoring system employs dynamic data flow modeling to simulate the behavior of probably militant behaviors. The contextual information (context data) includes such context as Pressure; Vehicle passing along the pipeline area, Manual digging, and Machine excavation. Dynamic simulations were performed using a real-time data obtained from the SPDC. The data is tested using AI system in MATLAB-SIMULINK environment to verify the performance of the proposed system. The results were promising indicating the real state of vandalism prediction.

show abstract

“…Risk analysis is commonly used to assist captains and ship owners in managing risk during a pirate attack. An application of particular relevance is a BN approach to risk management in offshore oil fields presented in Bouejla et al (2014).…”

Section: Maritime Piracy Applicationsmentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

View full text Add to dashboard Cite

A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

show abstract

A Bayesian network to manage risks of maritime piracy against offshore oil fields

Cited by 75 publications

References 10 publications

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Oil and Gas Pipeline Monitoring using Artificial Neural Network

A unified model for context-based behavioural modelling and classification

Contact Info

Product

Resources

About