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Matjaž Gams Bo štjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia

Intelligen t Access Control System Based On User behavior youtube.com/watch?v=W3rJVaBky9Y CIVABIS. Matjaž Gams Bo štjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia. Presentation. Motivation Experimental environment Entry events Architecture Modules Integration

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Matjaž Gams Bo štjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia

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  1. IntelligentAccess Control System Based On User behavioryoutube.com/watch?v=W3rJVaBky9Y CIVABIS Matjaž GamsBoštjan Kaluža, Erik Dovgan.. +10 Jožef Stefan institute, Slovenia

  2. Presentation • Motivation • Experimental environment • Entry events • Architecture • Modules • Integration • Verification • Discussion

  3. 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”

  4. Experimental environment Camera Fingerprint reader Card reader Door sensor

  5. Entry event • Card identification • Fingerprint verification • Door opens • Door closes • Unusual behavior • ̴ 10 additional scenarios in advance Bomb attack – only door opens A terrorist steals a card and a finger

  6. Architecture

  7. Access sensors and Time&Space software Fingerprint reader Camera Door sensor Card reader Videos Time&Space controller Camera module TCP/IP TCP/IP Intelligent system ODBC

  8. Module 1: Expert system • A set of̴ 10 predefined types of rules • Verifies if the events are “legal” • None of user behavior learning is used • Examples of generic rules: • alarm / warning if event between time1 and time2 • alarm / warning if more than N events in time • alarm / warning if no exit before time • alarm / warning if no exit in time

  9. Module 2: Micro learning • Learns user behavior on micro level – micro timing • Algorithm: Local outlier factor • Classification and explanation

  10. Module 3: Macro learning • Learns user behavior on macro level – macro timing / classification and explanation

  11. Module 3: Vision • Learns user behavior from video

  12. Integration Regular event Alarm event Main thread Expert system Micro learning Macro learning Camera Displaying final result

  13. Explanation

  14. Measurements • Our tests with our employees • Our “simulated” tests with our employees • Joint tests by security experts • perform several of them

  15. “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

  16. Measurements - results Statistic for regular accesses Statistic for irregular accesses Ok – 88% of regular accesses Alarm – 69% of irregular accesses

  17. 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

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