maritime security challenge - thales

Institut Mines-Télécom

Maritime security

challenge

London, 19th of January


Plan

1.Thales presentation

2.Context

3.Objectives

4.Data sources

5.Application fields & technical environment

6.Selection process and next steps

23/01/2015 Marine Traffic Data Challenge2


THALES presentation

Thales Group is a global technology leader for the Defence & Security and the Aerospace & Transport

markets.

The company generated revenues of €14bn annually. It employs over 67,000 employees in more than

50 countries.

The Center for Information Treatment and Analysis (CENTAI) lab within the Advanced Studies

Department of Thales envisions the future approaches, architectures and technologies for data analysis

and visualization.

The CENTAI team is especially focusing on developing and applying novel machine learning/statistical

techniques in multiple domains (Transport, Cyber Security, Social media …) where “big data” is often a

challenge.

Thales is working on the detection of abnormal behavior of marine traffic. The CENTAI lab team has

already developed a prototype. This prototype defines abnormal state patterns based on different tasks

fields (trajectory, stops, times of stops). This challenge proposes to develop a better abnormal behavior

detection system.



Maritime Security Context

The council of the European Union issued in June 2014 a

European Union maritime security strategy in which it

emphasizes the importance of maritime security for

Europe.

“More than 70% of the external borders of the Union are

maritime and hundreds of millions of passengers pass

through its ports each year. Europe's energy security

largely depends on maritime transport and

infrastructures.”

The main kinds of suspicious activities are:

• Illegal fishing (types of fish, fishing quota, fishing in

protected areas…)

• Organized crime activities: piracy, human, drug &

counterfeit goods trafficking

• Activities linked with nuclear proliferation

• Activities harming the environment: pollution due to

illegal or accidental discharge (fuel, chemical,

biological, nuclear products)



Objectives

The objective of Thales is to bring answers to European

maritime security by finding new ways of mining marine

traffic data in order to provide useful maritime security

services for national, European agencies but also for

companies vulnerable to maritime threats.

By bringing in this challenge a Thales owned dataset mixing

both private and open marine traffic related data, Thales

wants to help the data scientists to address the maritime

security challenges

Here are following examples suggested as study subject,

beside that Thales is open to any new fields or ideas or way

to improve maritime security.

• Suspicious boat trajectories detection in order to

maximize the efficiency of boat controls which are

currently made randomly. To accomplish this task

one of the sub-challenge which could be

interesting to investigate is the Prediction of boat

trip duration to detect anomalous durations

• Boat trajectories classification in order to

discriminate as finely as possible boat activates

according to their behaviors (if possible we wish to

go as deep as identifying the types of fish a fishing

boat is chasing)



Data Sources (1/2)

Thales datasets

• AIS (Automatic Identification System) data on the South East Asia (see picture below) area

collected on a 6-month period (18 million of messages). All boats above a given size are legally

required to have an AIS transmitter onboard. Each AIS transmitter has an id which uniquely

identifies the boat on which it is installed.

─ MMSI (id of the AIS device) – correlation with Lloyds open dataset to find the IMO number

(boat identifier)

─ Timestamp

─ Geolocation: latitude, longitude

─ Local trajectory: speed, heading, rate of turn

Open data*

• Equasis

─ EU blacklist

─ Safety control reports

• Lloyds register: vessel database

─ Ship id, type, length, draught

─ Engine type, number of engines

• Greenpeace

─ List of vessels and companies which have been recorded engaging in IUU fishing activities

(Illegal, Unregulated, Unreported)

─ Iranian oil tanker blacklist


* (will be aligned on the AIS in terms of time period)


Data Sources (2/2)

Weather data

• NOAA (National Oceanic and Atmospheric Administration): worldwide grid data but more precise

around the US (15km*15km vs 50km*50km). These data correspond to weather observation data

(not forecast data). Observations are updated every 3h and come from buoys and satellites.

─ Wind (direction, strength, gust, isDirectionVariable)

─ Current (direction, strength)

─ Temperature (surface and dew point)

─ Waves (height, speed, direction).

• OpenWeatherMap

─ API to collect data from NOAA for observations + forecast according to Canadian weather

model (forecast for all the observation data at T+3h, T+6h, T+12h, bad prediction after 3h for

the wind)

Additional datasets

• AIS data can be daily collected on public Websites such as MarineTraffic.com with dedicated

Web crawlers (1 million messages per day with a worldwide coverage). IMT has already done a

Proof of Concept of such a crawler. Such data could be used to enlarge the available dataset.

• Other interesting open datasets: wet market data (mostly available for the US. Availability to be

checked for South East Asia)

• Other datasets from public bodies, private companies are welcome

Global sizing: 6 months time period data, global sizing between 20 and 25 gigs



Application fields & technical environment

Primary application fields:

• Data management and data enrichment (time&space series, geographical information system)

• Detection algorithms and distributed machine learning algorithms (like MLIB/Sparkling Water)

Secondary application fields:

• Visualization/Presentation of suitable business oriented results (javascript libraries, mapbox)

• Performance optimization and scalability

Technical environment

• OS : CentOS, possibly other linux distributions

• Hadoop distribution : Hortonworks, possibly Cloudera

• Clusters : Spark, Storm, Elastic search…

• Workspace : Dedicated and secure workspace provided (https/ssh)


Performance optimization

Data visualization

Data

Management

Algorithms

& Machine

learning


Selection process and next steps

The team has to present a detailed methodology with enclosed references and research papers for the

detection of abnormal behavior of marine traffic.

Particular attention would be given to the good understanding of the issue, the algorithmic and

technical mastering and the expected output.

After the selection of candidates a monthly project meeting will be held with Thales and EIT ICT Labs

team. The candidates can apply until March 2015.


maritime security challenge - thales

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