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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
Presentation
• Motivation
• Experimental environment
• Entry events
• Architecture
• Modules
• Integration
• Verification
• Discussion
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”
Experimental environment
Door sensor
Card reader
Fingerprint reader
Camera
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
Architecture
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
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
Module 2: Micro learning
• Learns user behavior on micro level – micro timing
• Algorithm: Local outlier factor • Classification and explanation
Module 3: Macro learning
• Learns user behavior on macro level – macro timing / classification and explanation
Module 3: Vision
• Learns user behavior from video
Integration
Regular event Alarm event
Main thread
Expert system Micro learning Macro learning Camera
Displaying final result
Explanation
Measurements
• Our tests with our employees
• Our “simulated” tests with our employees
• Joint tests by security experts
• perform several of them
“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
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
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