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Network Security
Visualization
Research
Kulsoom Abdullah PhD
1
Kulsoom Abdullah's LinkedIn profile
https://www.linkedin.com/in/kulsoomabdullah
•  PhD Thesis work
•  Post-PhD work
Outline
2
•  Network traffic capacity is greater than
systems can process
•  Network attacks have not decreased,
current security tools are insufficient 
•  Information visualization techniques used
in network security research have initial
success and future promise 
•  Text logs and machine learning
algorithms are complemented and
information is represented more densely.
Why Information
Visualization & Network
Security
3
•  Graph occlusion
•  Avoid overlap and occlusion in the
visualizations
•  Scaling data parameters ranges
•  For networking, port numbers & IP addresses
need scaling
•  65535 TCP and UDP ports
•  4 billion possible IP addresses
•  Time scaling
•  Needs to be either small or large depending
on activity
•  small for quick activities: fast network scans,
DoS, fast propagating worms
•  large for slow network scans, overall trends
in a network 
Network data & graph
scaling issues
4
IDS Rainstorm
Main View

•  This design scales all of
the GT IP addresses
•  2.5 Class B addresses
plotted along 8 vertical
axis
•  20 IPs represented on
each line
•  Color represents
severity
•  Time: 24 hours of
Stealthwatch IDS alarms
shown
•  Most logs are archived
into 24 hours
•  Filtering on alarm type &
priority
 5
Zoom View
6
Examples
7
8!
ICMP Flood Example!
9
Worm
Watch port active alarms in
dorm space. Port watch was
on a known exploit.
•  All stated that time was saved compared to
using traditional IDS logs, it was simple and
intuitive to use & functioned well
•  Seeing patterns and sequence & connections
between host
•  Some did not return to the alarm text log &
some did return to the log for more detail
•  Not all alarm parameters are visually encoded 
•  Avoid overlap and occlusion in the
visualizations
•  Time scaling - Needs to be either small or large
depending on activity
•  small for quick activities: fast network scans,
DoS, fast propagating worms
•  large for slow network scans, overall trends in
a network 
Results-User Study
10
•  K. Abdullah, G. Conti and R. Beyah. “A Visualization Framework for Self-monitoring of
Web-based Information Disclosure” IEEE International Conference on Communications
(ICC), May 2008. (Post PhD) http://bit.ly/1WfUbu1
•  K. Abdullah, G. Conti and E. Sobiesk. “Self-monitoring of Web-based Information Disclosure”
Workshop on Privacy in the Electronic Society (WPES); October 2007. http://bit.ly/1WfUB3L
Cited in: G. Conti; Googling Security [http://amzn.to/1NcestF], Addison Wesley; November 2008.
(Post PhD) 
•  K. Abdullah, J. A. Copeland. ”High alarm count issues in IDS RainStorm” ACM Conference on
Computer and Communications Security’s Workshop on Visualization and Data Mining for
Computer Security (VizSEC); November 2006. http://bit.ly/1KioKbz
•  K. Abdullah, C. Lee, G. Conti and J. Copeland. “Processing Data to Construct Practical
Visualizations for Network Security” Information Assurance Newsletter, Information
Assurance Technology Analysis Center, United States Department of Defense, Summer 2006.
http://bit.ly/1Snimrt
•  G. Conti, K. Abdullah, J. Grizzard, J. Stasko, J. Copeland, M. Ahamad, H. Owen and C. Lee,
”Countering Security Analyst and Network Administrator Overload Through Alert and
Packet Visualization” IEEE Computer Graphics and Applications (CG&A), March 2006. http://
bit.ly/1ZqpgeD
•  K. Abdullah, C. Lee, G. Conti, J. Copeland and J. Stasko, “IDS RainStorm: Visualizing IDS
Alarms” IEEE Symposium on Information Visualization’s Workshop on Visualization for
Computer Security (VizSEC); October 2005. http://bit.ly/1n1Heb8 Cited in: G. Conti; Security
Data Visualization, No Starch Press; September 2007 http://amzn.to/1UVZPjY
•  K. Abdullah, C. Lee, G. Conti and J. Copeland, “Visualizing Network Data for Intrusion
Detection” IEEE Information Assurance Workshop (IAW); June 2005. http://bit.ly/1lg5m8P
•  G. Conti and K. Abdullah,“Passive Visual Fingerprinting of Network Attack Tools” ACM
Conference on Computer and Communications Security’s Workshop on Visualization and Data
Mining for Computer Security (VizSEC); October 2004. http://bit.ly/1PwRaR1
Publications
11
Post PHD
Network
Visualization
Research
3D Network Security Visualization 
12
3D Network
Security
Visualization
Troy Nunnally, CAP &
CSC at GATECH
•  3D Shows more
information vs 2D
•  3D can be difficult to
for Novices to
navigate
13
T. Nunnally, P. Chi, K. Abdullah, A. S. Uluagac, and R. A. Beyah, “P3D: A Parallel 3D Coordinate System for Advanced Network Scans”,
IEEE International Conference on Communications (ICC), Budapest, Hungary, September 2013
14!
Collaborative Filtering &
Global Restriction
15!
16!
T. Nunnally, K. Abdullah, A. S. Uluagac, and R. A. Beyah, “NAVSEC : A Recommender System
for 3D Network Security Visualizations”, IEEE Symposium on Information Visualization’s
Workshop on Visualization for Computer Security (VizSEC), Atlanta, GA, USA, October 2013.!
Stealthy Port
Scanning Use-Case
P3D Multiple concurrent FTP scan
 P3D Multiple concurrent FTP scan
17
•  Implementation and evaluation of more
advance use-case scenarios (i.e.,
introduce benign traffic) 
•  T. Nunnally, K. Abdullah, A. S. Uluagac, J. A. Copeland & R. A. Beyah,
"InterSec: An Interaction System for Network Security Applications",
IEEE Symposium on Information Visualization's Workshop on Visualization for
Computer Security (VizSEC) 2014. http://bit.ly/1P58Hpd
•  User Testing
•  3D, Stereoscopic view, Navigation
Assistance
•  Natural User Interface
•  Kinect, WII
Future P3D work
18

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PhD and Post PhD Network Security Visualization Research

  • 1. Network Security Visualization Research Kulsoom Abdullah PhD 1 Kulsoom Abdullah's LinkedIn profile https://www.linkedin.com/in/kulsoomabdullah
  • 2. •  PhD Thesis work •  Post-PhD work Outline 2
  • 3. •  Network traffic capacity is greater than systems can process •  Network attacks have not decreased, current security tools are insufficient •  Information visualization techniques used in network security research have initial success and future promise •  Text logs and machine learning algorithms are complemented and information is represented more densely. Why Information Visualization & Network Security 3
  • 4. •  Graph occlusion •  Avoid overlap and occlusion in the visualizations •  Scaling data parameters ranges •  For networking, port numbers & IP addresses need scaling •  65535 TCP and UDP ports •  4 billion possible IP addresses •  Time scaling •  Needs to be either small or large depending on activity •  small for quick activities: fast network scans, DoS, fast propagating worms •  large for slow network scans, overall trends in a network Network data & graph scaling issues 4
  • 5. IDS Rainstorm Main View •  This design scales all of the GT IP addresses •  2.5 Class B addresses plotted along 8 vertical axis •  20 IPs represented on each line •  Color represents severity •  Time: 24 hours of Stealthwatch IDS alarms shown •  Most logs are archived into 24 hours •  Filtering on alarm type & priority 5
  • 9. 9 Worm Watch port active alarms in dorm space. Port watch was on a known exploit.
  • 10. •  All stated that time was saved compared to using traditional IDS logs, it was simple and intuitive to use & functioned well •  Seeing patterns and sequence & connections between host •  Some did not return to the alarm text log & some did return to the log for more detail •  Not all alarm parameters are visually encoded •  Avoid overlap and occlusion in the visualizations •  Time scaling - Needs to be either small or large depending on activity •  small for quick activities: fast network scans, DoS, fast propagating worms •  large for slow network scans, overall trends in a network Results-User Study 10
  • 11. •  K. Abdullah, G. Conti and R. Beyah. “A Visualization Framework for Self-monitoring of Web-based Information Disclosure” IEEE International Conference on Communications (ICC), May 2008. (Post PhD) http://bit.ly/1WfUbu1 •  K. Abdullah, G. Conti and E. Sobiesk. “Self-monitoring of Web-based Information Disclosure” Workshop on Privacy in the Electronic Society (WPES); October 2007. http://bit.ly/1WfUB3L Cited in: G. Conti; Googling Security [http://amzn.to/1NcestF], Addison Wesley; November 2008. (Post PhD) •  K. Abdullah, J. A. Copeland. ”High alarm count issues in IDS RainStorm” ACM Conference on Computer and Communications Security’s Workshop on Visualization and Data Mining for Computer Security (VizSEC); November 2006. http://bit.ly/1KioKbz •  K. Abdullah, C. Lee, G. Conti and J. Copeland. “Processing Data to Construct Practical Visualizations for Network Security” Information Assurance Newsletter, Information Assurance Technology Analysis Center, United States Department of Defense, Summer 2006. http://bit.ly/1Snimrt •  G. Conti, K. Abdullah, J. Grizzard, J. Stasko, J. Copeland, M. Ahamad, H. Owen and C. Lee, ”Countering Security Analyst and Network Administrator Overload Through Alert and Packet Visualization” IEEE Computer Graphics and Applications (CG&A), March 2006. http:// bit.ly/1ZqpgeD •  K. Abdullah, C. Lee, G. Conti, J. Copeland and J. Stasko, “IDS RainStorm: Visualizing IDS Alarms” IEEE Symposium on Information Visualization’s Workshop on Visualization for Computer Security (VizSEC); October 2005. http://bit.ly/1n1Heb8 Cited in: G. Conti; Security Data Visualization, No Starch Press; September 2007 http://amzn.to/1UVZPjY •  K. Abdullah, C. Lee, G. Conti and J. Copeland, “Visualizing Network Data for Intrusion Detection” IEEE Information Assurance Workshop (IAW); June 2005. http://bit.ly/1lg5m8P •  G. Conti and K. Abdullah,“Passive Visual Fingerprinting of Network Attack Tools” ACM Conference on Computer and Communications Security’s Workshop on Visualization and Data Mining for Computer Security (VizSEC); October 2004. http://bit.ly/1PwRaR1 Publications 11
  • 13. 3D Network Security Visualization Troy Nunnally, CAP & CSC at GATECH •  3D Shows more information vs 2D •  3D can be difficult to for Novices to navigate 13
  • 14. T. Nunnally, P. Chi, K. Abdullah, A. S. Uluagac, and R. A. Beyah, “P3D: A Parallel 3D Coordinate System for Advanced Network Scans”, IEEE International Conference on Communications (ICC), Budapest, Hungary, September 2013 14!
  • 16. 16! T. Nunnally, K. Abdullah, A. S. Uluagac, and R. A. Beyah, “NAVSEC : A Recommender System for 3D Network Security Visualizations”, IEEE Symposium on Information Visualization’s Workshop on Visualization for Computer Security (VizSEC), Atlanta, GA, USA, October 2013.!
  • 17. Stealthy Port Scanning Use-Case P3D Multiple concurrent FTP scan P3D Multiple concurrent FTP scan 17
  • 18. •  Implementation and evaluation of more advance use-case scenarios (i.e., introduce benign traffic) •  T. Nunnally, K. Abdullah, A. S. Uluagac, J. A. Copeland & R. A. Beyah, "InterSec: An Interaction System for Network Security Applications", IEEE Symposium on Information Visualization's Workshop on Visualization for Computer Security (VizSEC) 2014. http://bit.ly/1P58Hpd •  User Testing •  3D, Stereoscopic view, Navigation Assistance •  Natural User Interface •  Kinect, WII Future P3D work 18