Real Time and Forensic Network Data Analysis Using Animated Combined Visualizations Sven Krasser Gregory Conti Julian Grizzard Jeff Gribschaw Henry Owen Georgia Institute of Technology
Overview of Visualization
Motivation High level analysis - low level discovery Complement Ethereal by providing big picture context TIVO for Network Traffic Dealing with customers Network behavior / Intruder behavior Support Honeynet log analysis Not real-time intrusion detection (yet)
System Design real time packet capture and forensic playback navigate forwards and backwards in dataset 3D and 2D views Open GL and commodity hardware (P4 2.5GB) Parallel coordinate plot adjacent to two animated displays
Overview and Detail
Routine Honeynet Traffic (baseline)
Slammer Worm
Constant Bitrate UDP Traffic
Port Sweep
Attempted HTTP Attack…
Attempted HTTP Attack… (zoom)
Compromised Honeypot
Attacker Transfers Three Files…
campus network
Inbound Campus Traffic (5 seconds)
Campus Network Traffic (10 msec capture) inbound outbound
botnet visualization
Combined botnet/honeynet traffic
System Performance
Conclusions Combining of visualization techniques Open GL and commodity hardware Significant analyst performance gains Interaction techniques Distinct visual signatures –Smart Books Tipping point on high volume networks –Honeynet /CTF analysis possible now –Prefiltering required for general purpose use
Future Work Semantic zoom –packets -> flows -> application/protocol specific Work through slices of network traffic –allow user to focus on what is interesting Maximize customization and interaction –Filtering and encoding –All fields Multiple data streams Knowledge discovery Help highlight what is interesting Easily drop in different windows on network traffic –look at traffic from different perspectives Evaluation
Demo of tools
Acknowledgements Charles Robert Simpson for providing packet capture source code David Dagon for for providing the botnet data
Questions? Image: Sven Krasser Gregory Conti Julian Grizzard Jeff Gribschaw Henry Owen Paper