Data mining for anomaly detection in maritime traffic data

For the past few years, oceans have become once again, an important means of communication and transport. In fact, traffic density throughout the globe has suffered a substantial growth, which has risen some concerns. With this expansion, the need to achieve a high Maritime Situational Awareness (MSA) is imperative. At the present time, this need may be more easily fulfilled thanks to the vast amount of data available regarding maritime traffic. However, this brings in another issue: data overload. Currently, there are so many data sources, so many data to obtain information from, that the operators cannot handle it. There is a pressing need for systems that help to sift through all the data, analysing and correlating, helping in this way the decision making process. In this dissertation, the main goal is to use different sources of data in order to detect anomalies and contribute to a clear Recognised Maritime Picture (RMP). In order to do so, it is necessary to know what types of data exist and which ones are available for further analysis. The data chosen for this dissertation was Automatic Identification System (AIS) and Monitorização Contínua das Atividades da Pesca (MONICAP) data, also known as Vessel Monitoring System (VMS) data. In order to store 1 year worth of AIS and MONICAP data, a PostgreSQL database was created. To analyse and draw conclusions from the data, a data mining tool was used, namely, Orange. Tests were conducted in order to assess the correlation between data sources and find anomalies. The importance of data correlation has never been so important and with this dissertation the aim is to show that there is a simple and effective way to get answers from great amounts of data.Nos últimos anos, os oceanos tornaram-se, mais uma vez, um importante meio de comunicação e transporte. De facto, a densidade de tráfego global sofreu um crescimento substancial, o que levantou algumas preocupações. Com esta expansão, a necessidade de atingir um elevado Conhecimento Situacional Marítimo (CSM) é imperativa. Hoje em dia, esta necessidade pode ser satisfeita mais facilmente graças à vasta quantidade de dados disponíveis de tráfego marítimo. No entanto, isso leva a outra questão: sobrecarga de dados. Atualmente existem tantas fontes de dados, tantos dados dos quais extrair informação, que os operadores não conseguem acompanhar. Existe uma necessidade premente para sistemas que ajudem a escrutinar todos os dados, analisando e correlacionando, contribuindo desta maneira ao processo de tomada de decisão. Nesta dissertação, o principal objetivo é usar diferentes fontes de dados para detetar anomalias e contribuir para uma clara Recognised Maritime Picture (RMP). Para tal, é necessário saber que tipos de dados existem e quais é que se encontram disponíveis para análise posterior. Os dados escolhidos para esta dissertação foram dados Automatic Identification System (AIS) e dados de Monitorização Contínua das Atividades da Pesca (MONICAP), também conhecidos como dados de Vessel Monitoring System (VMS). De forma a armazenar dados correspondentes a um ano de AIS e MONICAP, foi criada uma base de dados em PostgreSQL. Para analisar e retirar conclusões, foi utilizada uma ferramenta de data mining, nomeadamente, o Orange. De modo a que pudesse ser avaliada a correlação entre fontes de dados e serem detetadas anomalias foram realizados vários testes. A correlação de dados nunca foi tão importante e pretende-se com esta dissertação mostrar que existe uma forma simples e eficaz de obter respostas de grandes quantidades de dado

A semi-supervised learning framework based on spatio-temporal semantic events for maritime anomaly detection and behavior analysis

International audienceDetection of abnormal movements of mobile objects has recently received a lot of attention due to the increasing availability of movement data and their potential for ensuring security in many different contexts. As timely detection of these events is often important, most current approaches use automated data-driven approaches. While these approaches have proved to be effective in specific contexts, they are not easily accepted by operators in charge of surveillance due, among other reasons, to the lack of user involvement during the detection process. To improve the detection and analysis of maritime anomalies this paper explores the potential of spatial ontologies for modeling maritime operator knowledge. The goal of this research is to facilitate the integration of human knowledge by modeling it in the form of semantic rules to improve confidence and trust in the anomaly detection system

GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach-referred to as GeoTrackNet-for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Anomaly detection in Multivariate Temporal Data for Vessels Abnormal Behaviour Detection

The growing number of deployed data mining systems leverage the interest in temporal data anomaly detection. From cyber-security or finance to heart-diseases detection, unexpected data often incorporate critical information that must be analysed. Data anomalies have long been studied from an univariate perspective where only one data dimension changes over time. Few works have been dedicated to multivariate anomaly detection. In this work we provide a comprehensive and structured analysis of the main definitions, state-of-art methods and approaches focusing multivariate temporal data anomaly detection. Our research focus on dealing with variable length data series with millions of samples and multiple feature categories, either static or dynamic, real or categorical valued. We describe a case-study in the maritime domain investigating the unusual spatio-temporal behaviour of commercial vessels and experiment over two open datasets and one got from the MARISA H2020 Project1

Contextual Knowledge and Information Fusion for Maritime Piracy Surveillance

Proceedings of: NATO Advanced Study Institute (ASI) on Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, Salamanca, 19-30 September, 2011Though piracy accounts for only a small fraction of the general losses of the maritime industry it creates a serious threat to the maritime security because of the connections between organized piracy and wider criminal networks and corruption on land. Fighting piracy requires monitoring the waterways, harbors,and criminal networks on the land to increase the ability of the decision makers to predict piracy attracts and manage operations to prevent or contain them. Piracy surveillance involves representing and processing huge amount heterogeneous information often uncertain, unreliable, and irrelevant within a specific context to detect and recognize suspicious activities to alert decision makers on vessel behaviors of interest with minimal false alarm. The paper discusses the role of information fusion, and context representation and utilization in building an piracy surveillance picture.This paper has utilized the results of the research activity supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC and CAM CONTEXTS (S2009/TIC-1485)Publicad

Automatic human behaviour anomaly detection in surveillance video

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions