1. Artificial Intelligence Center,
Mayukh Dass
Artificial Intelligence Center,
University of Georgia
Athens,Georgia,
U.S.A.

2. Contents What is Intrusion Detection? How it is affecting the society?
What are the present techniques used?
What is new in LIDS?
Why should we use autonomous agents?
What are the components of LIDS?
Is LIDS working?
What is left to do in future?

3. Intrusion Detection Problem of identifying unauthorized users.
Protect the system from being compromised.
2 categories:
Misuse Detection.
Anomaly Detection.
Revenue loss in 2002 = $455,848,000
(CSI/FBI Computer Crime and Security Survey, 2002.)

4. Invaders of the civilization

5. Altruistic side of hacking

6. Next-generation hackers

7. Intrusions provide jobs

8. Intrusion Detection Techniques
Rule-based.
Data Mining.
Artificial Neural Network.
Genetic Algorithm.
Statistical Methods.
Agent framework:
Autonomous Agents.
Intelligent Agent.
Mobile Agents.
Mapping Human Immunization

9. Commercial Intrusion Detection Systems
they are rule based.
high maintenance cost.
not very reliable.
large number of false positive alerts.
not very flexible.
non-scalable (snort : for “average” system).
high overall cost.
Example : Snort, SHADOW, and so on..

10. Reliable Network Security System. What??

11. Features of LIDS: Learning Intrusion Detection System.
Reliable.
Flexible.
Behavior based.
Blackboard-based architecture.
controlled by autonomous agents.
Learning and adapting capability.
Low maintenance cost.
Uses building blocks of computational intelligence as intrusion analyzer.
Low rate of false positive alarm.

12. Why should we use Autonomous Agents for detecting Intrusion in the network ?
Runs continually.
Fault tolerant.
Resist subversion (monitor itself)
Minimal overhead
Configurable
Adaptable
Scalable
Graceful degradation of service
Dynamic reconfiguration.

14. Autonomous Agents Network Reader Initial Analyzer Initial Alert Agent
System data Reader
Attack Classifier (GA-based filter)
ANN Analyzer
Teaching Agent
Report Generator

15. GENERATED REPORTS

18. Future Directions Complete building the learning agent of LIDS.
Test LIDS in a more complex environment.
Add new functionalities like visual representation of the reports.
Try to increase the speed and optimization of the processes.

19. Acknowledgement Dr. J. Cannady Dr. D. Potter. Dr. D. Nute.
Dr R. McClendon