Matjaž Gams Bo štjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia
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Transcript of Matjaž Gams Bo štjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia
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Intelligent Access Control System Based
On User behavior youtube.com/watch?v=W3rJVaBky9Y CIVABIS
Matjaž Gams Boštjan Kaluža, Erik Dovgan.. +10
Jožef Stefan institute, Slovenia
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Presentation
• Motivation
• Experimental environment
• Entry events
• Architecture
• Modules
• Integration
• Verification
• Discussion
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Motivation (security project)• Terrorist attacks – bypass sensors
• Malitious employee – drunk, angry ...
intercept unusual events based on intelligent experience
•2 people entering, one registered•employee “afraid”
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Experimental environment
Door sensor
Card reader
Fingerprint reader
Camera
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Entry event1) Card identification
2) Fingerprint verification
3) Door opens
4) Door closes
• Unusual behavior
• ̴; 10 additional scenarios in advance
Bomb attack – only door opens
A terrorist steals a card and a finger
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Architecture
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Access sensors and Time&Space software
Card reader
Fingerprint reader
Door sensor
Time&Space controller
Intelligent system
Camera
Camera module
Videos
TCP/IP
TCP/IP
ODBC
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Module 1: Expert system
• A set of ; 10 predefined types of rules
• Verifies if the events are “legal”
• None of user behavior learning is used
• Examples of generic rules:
1) alarm / warning if event between time1 and time2
2) alarm / warning if more than N events in time
3) alarm / warning if no exit before time
4) alarm / warning if no exit in time
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Module 2: Micro learning
• Learns user behavior on micro level – micro timing
• Algorithm: Local outlier factor • Classification and explanation
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Module 3: Macro learning
• Learns user behavior on macro level – macro timing / classification and explanation
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Module 3: Vision
• Learns user behavior from video
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Integration
Regular event Alarm event
Main thread
Expert system Micro learning Macro learning Camera
Displaying final result
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Explanation
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Measurements
• Our tests with our employees
• Our “simulated” tests with our employees
• Joint tests by security experts
• perform several of them
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“Simulated” Measurements
• Tested modules: Expert rules, micro learning and macro learning
• Create regular accesses: Five people, each 40 learn and 10 test accesses –
• Create irregular accesses: Fake-identity experiment – generate entries with identification card of another person
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Measurements - resultsok warning alarm
rules 100% 0% 0%
micro 98% 2% 0%
macro 90% 10% 0%
together 88% 12% 0%
ok warning alarm
rules 100% 0% 0%
micro 36% 15% 50%
macro 14% 25% 62%
together 13% 18% 69%
Statistic for regular accesses
Statistic for irregular accesses
Ok – 88% of regular accesses
Alarm – 69% of irregular accesses
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Conclusion
• Designed and tested an original ambient-inteligence system for entry control based on user behavior
• It integrates arbitrary (currently four) independent modules and sensors
• Significant increase in security
• Patent pending, real-life application