1. Flow Anomaly Detection in Firewalled Networks Research Report Mike Chapple December 15, 2005

2. The Problem Intruders are Clever!

3. Firewall Anomaly Detection

4. FADS Architecture

5. Forecast Development Evaluation Criteria –Number of connections –Bytes to client –Bytes to server Data Segmentation –Six time segments –Weekday traffic only

6. Modeling Techniques 1020 3040 50 Average 30 Standard Deviation 14 Forecast Range 9-51 Median 30 Interquartile Range 20 Forecast Range 0-60 1020 3040 6,000 Average 880 Standard Deviation 2,257 Forecast Range -2,507 – 4,267 Median 30 Interquartile Range 20 Forecast Range 0-60

7. Evaluation Goals Determine whether FADS produces a manageable number of alerts Evaluate impact of external traffic Examine three case studies for evidence of system effectiveness Demonstrate performance is within bounds of feasibility

8. Goal #1: Feasibility 1.5IQ1.5SD3IQ3SD Normal7797722982488490 Overflow962958738388 Underflow6851066458375

9. Goal #2: Impact of External Traffic

10. Goal #3: Case Studies Underflow alerts to a web server supporting academic functions Overflow events to a reporting server in production datacenter Overflow events related to file integrity monitoring

11. Goal #4: Performance Feasible to port this system to an online application –Processing 6-hour log file < 10 minutes –Forecasts generated in < 30 seconds –Evaluation dataset processed in ~ 4 seconds

12. Future Work Evaluation with extended dataset Advanced modeling techniques including periodicity Dynamic selection of time segments Automation of processing for online analysis

13. Questions?