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