1. Grant Pannell

2. Intrusion Detection Systems  Attempt to detect unauthorized activity  CIA – Confidentiality, Integrity, Availability  Commonly network-based  Obsolete? Network traffic encryption  Moving to host-based  Honeypots (emulated services)  Application’s execution flow  Behavior of the user

3. Detection Methods  Misuse Detection  Rule-based  User states: I use Notepad, not WordPad  Low false-positives, high detection  Can’t predict and learn how a user behaves  Anomaly Detection  Gather audit data (user’s actions) over time  Analyze with statistical methods  Create a profile – User uses Notepad, system learns  Higher false-positives, lower detection rate  Combination of both is best

4. Profiling a User  Must determine “normal” behavior for anomaly detection  User Profile  Characteristics:  Applications running  Number of Windows, Number of Processes  Performance of running applications (CPU usage)  Keystrokes (delays, speed)  Websites visited

5. Motivation  Determine unauthorized use  Adoption of encryption of network traffic  Multiple characteristics  Previous studies focus on single characteristics for profiling  Microsoft Windows - graphical user interface  Previous studies focus on command usage

6. So, what is it exactly? A behavioral host-based intrusion detection system That profiles a user, using multiple characteristics… To detect unauthorized use of a machine … That will run on Microsoft Windows, to take advantage of GUI characteristics

7. Research Questions  Is it possible? Feasible? Real-world?  Possible in a graphical user interface environment?  Combination of characteristics improves performance?  Taxes system resources?  Detection performance?  Low false-positives (disallowed authorized users)  High detection rate (disallowed intruders)  Detect in a practical amount of time?

8. Literature Review  Not much research in the public domain…  Behavioural Intrusion Models  Dates back to 1980 by Anderson  Manually collect Audit Trails from machines  Track file and resource access  Furthered by Denning (1987)  Detailed model of Anderson’s work  Tan (1995), Gunetti et al.(1999), Balajinath et al. (2001), Pillai (2004)  All based on UNIX  Characterizes by command usage or performance (CPU, Memory, I/O, etc.)  Different due to the learning algorithm used

9. Methodology  Developed System  Developed in Microsoft.NET C#  Allow each characteristic to be “snapped-in”  Extensive logging output for analysis and testing  7 Systems Test  2 “Power Users” (Win7 x64, XP x64)  2 Office Based (2x XP x86)  1 Gaming (Vista x64)  2 Web Browsing (Vista x86, XP x86)

10. Methodology  Learning Mode for ~10 days  System worked for 28880 collections then disabled itself  “Perfect” Learning  All false positives  Decreasing false-positives over time (learning)  Detection Mode after 10 days  Only used to break the profile  Used to determine how long it takes to break the profile  Stress test each characteristic

11. Prototype Architecture

12. Algorithms  CPU & Memory Usage  3 Techniques:  Standard Deviation (0.5 Pts) (Last 120 Values)  Rolling Average (1 Pts (Overall)  Sliding Limit (2 Pts) (Overall)  Websites Viewed  Can only check if user visits new sites, not if revisiting them  Rolling average  New sites per hour, but check every 30 seconds  Works for learning two cases  Many new sites per hour  No new sites per hour

13. Algorithms  Number of Windows  Wanted to check Window Titles and Positions  Titles, never static (i.e. “ - MS Word”)  Positions, seeming random for most windows  Rolling average like Websites Viewed  Not always accurate  Number of Processes  Sliding limits  Fully learned profile should include all processes  Therefore deny all new?

14. Algorithms  Keystroke Usage  Use digraphs  D->i, i-> g, g->r, r->a, a->p, p->h, h->s  Delay between digraphs  Standard Deviations  Collect last 100 values  Overall Scoring System  Directly related to User Activity (2 Pts)  Keystrokes, Number of Windows, Websites Viewed  Indirectly related (Application Profiling) (1 Pt)  CPU Usage, Memory Usage, Number of Processes

15. False-Positives vs. Number of Collections (Time) (CPU Usage)

16. False-Positives per Machine (Memory Usage)

17. False-Positives per Machine (Num Windows)

18. False-Positives vs. Number of Collections (Time) (Websites Viewed)

19. False-Positives vs. Number of Collections (Time) (Keystroke Usage)

20. False-Positives vs. Number of Collections (Time) (Overall Scoring)

21. False Positive Rate per Characteristic

22. Results - Intrusions  Test intrusions in Detection Mode  Trying to trigger each characteristic  Keystrokes – another user’s patterns  Only using mouse to open many new processes and windows  Use running processes, attempt abnormalities  Completely new user on same profile  Scoring system  5 point maximum  2 points for directly related  1 point for indirectly related  Minimum 3 accumulations (3*30 secs) to trigger

23. Average Time to Detect Intrusions per Intrusion Test

24. Further Research  Time block testing  Categorization  Mouse clicks  More complex learning algorithms  Intruder has physical access to the machine  System Performance

25. Conclusion  Is it possible? Feasible? Real-world?  Better on directly related characteristics  Possible in a graphical user interface environment?  GUI objects turned out to be not as useful as first proposed  Combination of characteristics improves performance?  Scoring system lowers false-positives  Taxes system resources?  Large history, real-time typing analysis could be better  Detection performance?  Highest false-positive rates at 4.5% with a malfunctioning characteristic  Detect in a practical amount of time?  90 - 180 second detection times

26. Questions? ?