tresspass: simulation and field tests for outline risk
TRANSCRIPT
Outline
TRESSPASSrobusT Risk basEd Screening and alert System for PASSengers and luggage
is funded by the Horizon 2020 Framework Programme of the European Union for Research and Innovation.Grant Agreement number: 787120 — TRESSPASS — H2020-SEC-2016-2017/H2020-SEC-2016-2017-2
TRESSPASS: Simulation and Field Tests for Risk-based BCP security and integrated EU border management
EAB-RPC 2020
Virtual Conference
2020-09-14
Dimitris M. Kyriazanos, PhD
NCSR Demokritos
Outline
TRESSPASS - robusT Risk basEd Screening and alert System for Passengers and luggage
Funded by the Horizon H2020 Framework Programme of the European Union for Research and Innovation,
Grant Agreement number: 787120
Consortium
Project coordinator
Project Coordinated by:
Integrated Systems Laboratory
Institute of Informatics & Telecommunications
National Center for Scientific Research “Demokritos”
Coordinator: Stelios C. A. Thomopoulos, PhD
Dimitris M. Kyriazanos, PhD (deputy)
OutlineROBUST RISK BASED SCREENING AND ALERT SYSTEM FOR PASSENGERS AND LUGGAGE
Abstract: TRESSPASS project includes air, maritime and land border crossing points, andalso travel routes that combine different modalities. It excludes border crossings outsideof border crossing points. With regards to threats, this includes smuggling, irregularimmigration, cross border crime, and terrorism. The scope of TRESSPASS is multi threat,multimodal and includes all tiers of access model:
1. measures undertaken with third countries or service providers;
2. cooperation with neighboring countries;
3. border control and counter-smuggling measures;
4. control measures within the area of free movement.
Tiers
Tier 4
Tier 3
Tier 2
Tier 1
Grant Agreement N° : 787120Research category: Innovation ActionTotal Budget: 9.299.391,25EU Contribution : 7.901.470,75 Started: June 2018 End: November 2021
The Scope
OutlineROBUST RISK BASED SCREENING AND ALERT SYSTEM FOR PASSENGERS AND LUGGAGE
Pilot 1, Air Border, Schiphol airport, Amsterdam
Pilot 2, Land Border, Dorohusk, Poland
Pilot 3, Sea Border, Piraeus Port, Greece
Pilots
© 2018. NCSR Demokritos. All Rights Reserved.
OutlineROBUST RISK BASED SCREENING AND ALERT SYSTEM FOR PASSENGERS AND LUGGAGE
Legal – Regulatory – Policy Framework
Ethics & Data Protection – GDPRCompliance – Ethics Audit
Informed Consent/Volunteers – soundnessof scientific methodology – TRL 7
Availability of real data
Key challenges
5
Outline
Distributed Messaging System
Ingestion Services
Data Fusion & Dynamic Risk Assessment
Simulation
System Architecture
Outline
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Data Fusion component corresponds to the part of the Risk Estimation pipeline responsible for reducing Risk Indicator estimations from various sources into more accurate ones with higher level of confidence.
The Risk Estimation flow procedure is defined as following:
Data Fusion Methodology
Sensing devices Data FusionRisk estimation based on Risk
IndicatorsFinal Risk
Data Fusion
Risk Indicator 1
Risk Indicator 2
Risk Indicator 3
Risk Indicator N
Outline
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The added value of such an infrastructure relies on the capability of estimating in real-time potential risks of undesired incidents based on behavioral characteristics as acquired by surveillance non-intrusive monitoring systems.
Use case examples
Suspicious Loitering
1. Input: presence/location sensing, use of Passenger app (or not), security personnel mobile app, geo-located information of risk
2. Output: probability of suspicious loitering/malicious intent or harmless situation
High-risk Travel pattern
1. Input: PNR info (itinerary, ticket purchase info), use of Passenger app (or not), RFID luggage tracking, security personnel mobile app, Dark Web info
Data Fusion Use Cases
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Field Testing & DPIA risks
Risk
ID
Processes that involve risks include: Likelihood of
harm
Severity of
harm
Overall
riskR1 Location sensing becomes too
obtrusive, reveals too much
information about the passenger
location
Possible Minimal Low-
Mediu
m
R2 Opportunistic/random disclosure of
personal data during tests – e.g. non
participants passing by, screens and
monitors unattended etc
Remote Minimal Low
R3 Hacking and digital data theft Possible Minimal to
Significant
Mediu
mR4 Physical theft of Field Test equipment
and subsequent data theft
Remote Minimal to
significant
Mediu
mR5 Processing special categories of data
and involuntary disclosure of such
information: wheelchair option
Remote Significant Low-
Mediu
m
Outline
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Risk Based BCP Simulation (iCrowd)
https://vimeo.com/155102249
Outline
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BCP Simulation
https://vimeo.com/155102249
An integrated simulation platform for operational flow and passenger/personnel crowd simulation
• User-defined simulation scenarios
• Sophisticated crowd engine and collision avoidance
• Multiple behaviour models
• Distributed simulation (external modules, multiple engines, load distribution)
• C2/Web Portal Integration
• Providing synthetic data
• Integration with third-party simulators
• Risk Assessment
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Monte-Carlo simulator
• Discrete event Monte Carlo simulation for component initialization • Risk aggregation Dynamic risk evaluation• Formulation of simple risk assessment methods.• Sensitivity study of the tool for arbitrary component models
• Development of a shared evaluation platform which encompass all the modules and functionality including the KPI calculation using Monte-Carlo simulator & checkpoint design tool
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•Automated risk assessment for airport passengers
• Leads to automated suspicious behavior detection
•Issues
• What data to use (GDPR compliance)
• What constitutes risky/anomalous behaviors
• What sensors to deploy, where and how (GDPR compliance)
• What is the associated cost of investment
• How to assess trade off between cost of investment and benefits from risk assessment
•Methodology for risk assessment based on
• Deep learning network architecture
• iCrowd behavior simulator
• Risk assessment methodology framework
• Trade off analysis between investment cost and risk assessment benefits
Simulation for risk analysis & assessment (i)
Outline
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•Deep Networks:
• Can be used for one-class (normal behavior) training
• Defining and detecting abnormal behavior/anomaly
•iCrowd simulator:
• Passenger trajectories at the BCP
• Generated unlimited normal synthetic training and testing data
https://doi.org/10.1117/12.2519857
https://doi.org/10.1109/AVSS.2019.8909844
Simulation for risk analysis & assessment (ii)
Actual Crowd picture at airport check in
iCrowd photorealistic representation at airport check in
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Simulation: Demo Video
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Simulation –Key Benefits and conclusions
• Easily extend - adapt security perimeter
• BCP deployment optimization/ cost-benefit
• Benchmarking, KPIs related to CONOPS, acceptance and performance
• Computer vision training: presence/movement/(re)identification
• Microexpressions not feasible
• Trajectory/movement tracking
• Multiple agents & behaviours supported
Outline
TRESSPASS - robusT Risk basEd Screening and alert System for Passengers and luggage
Funded by the Horizon H2020 Framework Programme of the European Union for Research and Innovation,
Grant Agreement number: 787120
Consortium
Project coordinator
Dimitris M. Kyriazanos
dkyri @iit.demokritos.gr
Office: +30 210 650 3150
Thank You!Thank You
Stelios C. A. Thomopoulos
Office: +30 210 650 3155