passive visual fingerprinting of network attack tools gregory conti kulsoom abdullah college of...

29
Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Upload: dwain-lyons

Post on 16-Jan-2016

214 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Passive Visual Fingerprinting of Network Attack Tools

Gregory ContiKulsoom Abdullah

College of ComputingGeorgia Institute of Technology

Passive Visual Fingerprinting of Network Attack Tools

Gregory ContiKulsoom Abdullah

College of ComputingGeorgia Institute of Technology

Page 2: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Motivation

Common network reconnaissance and vulnerability assessment tools can be visualized in such a way as to identify the attack tool used.

•Law enforcement forensics

•Identify characteristics of new tools/worms

•Provide insight into attacker’s methodology & experience level

•Help network defender to initiate appropriate response

Page 3: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Ethernet

Packet Capture

Parse

Process

Plot

tcpdump(pcap, snort)

Perl

Perl

xmgrace(gnuplot)

tcpdumpcapturefiles

winpcap

VS

VS

VS Interact

System Architecture

Page 4: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Ethernet: http://www.itec.suny.edu/scsys/vms/OVMSDOC073/V73/6136/ZK-3743A.gif

Link Layer (Ethernet)

Network Layer (IP)

Examining Available Data…

Transport Layer (TCP)

Transport Layer (UDP)

IP: http://www.ietf.org/rfc/rfc0791.txt

TCP: http://www.ietf.org/rfc/rfc793.txtUDP: http://www.ietf.org/rfc/rfc0768.txt

All raw data available on the wire:

• Application layer data

• Transport layer header

• Network layer header

• Link layer header

Focused on: • Source / Destination Port• Source / Destination IP• Timestamp• Length of raw packet• Protocol Type

Page 5: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Attacks Fingerprintednessus 2.0.10

nmap 3.0

nmap 3.5

nmapwin 1.3.1

Superscan 3.0

Superscan 4.0

nessus 2.0.10

nikto 1.32

scanline 1.01

sara 5.0.3

NSA CDX dataset 2003

http://www.insecure.org/tools.html

Page 6: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Visualizations• Time Sequence Data

– Sequence of Source/Destination Ports and IP’s– Sequence of Packet Lengths– Sequence of Packet Protocols

• Port and IP Mapping– Source Port to Destination Port – Source IP to Destination IP – Source IP to Destination Port– Source Port/IP to Destination IP/Port – Source IP/Port to Destination Port/IP

• Characterization of home/external network

Page 7: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

External Port Internal Port

65,535 65,535

0 0

External IP Internal IP

255.255.255.255 255.255.255.255

0.0.0.0 0.0.0.0

External IP Internal Port

255.255.255.255 65,535

0.0.0.0 0

parallel plot views

Page 8: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Baseline

External Port Internal Port External IP Internal IP

Page 9: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

nmap 3 (RH8)

NMapWin 3 (XP)

SuperScan 3.0 (XP)

SuperScan 4.0 (XP)

nmap 3 UDP (RH8)

nmap 3.5 (XP)

scanline 1.01 (XP)

nikto 1.32 (XP)

Page 10: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Sara 5.0.3(port to port)

Light Medium Heavy

Page 11: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Georgia Tech Honeynet

External IP Internal Port External Port Internal Port External IP Internal IP

Page 12: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

External IP External Port Internal Port Internal IP

255.255.255.255 65,535 65,535 255.255.255.255

0.0.0.0 0 0 0.0.0.0

Also a Port to IP to IP to Port View

Page 13: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Exploring nmap 3.0 in depth(port to IP to IP to port)

default (root) stealth FIN (-sF) NULL (-sN)

SYN (-sS -O) stealth SYN (-sS) CONNECT (-sT)

UDP (-sU)

XMAS (-sX)

Page 14: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

nmap within Nessus (port to IP to IP to port)

CONNECT (-sT)

UDP (-sU)

Nessus 2.0.10

Page 15: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

SuperScan Evolution (port to IP to IP to port)

SuperScan 3.0

scanline 1.01

SuperScan 4.0

Page 16: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

packet length and protocol type over time

port

s

packe

tslength

Page 17: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

WinNMap

Page 18: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

SuperScan 4.0

Page 19: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

time sequence data(external port vs. packet)

nmap win superscan 3

port

s

port

spackets packets

Also internal/external IP and internal port

Page 20: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

tool interface

Page 21: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology
Page 22: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Findings (Weaknesses)

• Interaction with personal firewalls• Countermeasures• Scale / labeling are issues• Occlusion is a problem• Greater interactivity required for forensics and less

aggressive attacks• Some tools are very flexible• Source code not available for some tools

Page 23: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Findings (Strengths)

• Aggressive tools have distinct visual signatures• Threading / multiple processes may be visible• Some source code lineage may be visible• Some OS/Application features are visible • Some classes of stealthy attack are visible

Page 24: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Findings (Strengths)

• Sequence of ports scanned visible• Frequently attacked ports visible• Resistant to high volume network traffic• Viable in the presence of routine traffic• Useful against slow scans (hours-weeks)• Useful against distributed scans

Page 25: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Future Work

• Add forensic capability

• Task driven interactivity (Zoom & filter, details on demand)

• Smart books (images & movies)

• Usability studies

• Stress test

• Explore less aggressive attack classes

Page 26: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Demo

Page 27: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

classic infovis surveywww.cc.gatech.edu/~conti

security infovis surveywww.cc.gatech.edu/~conti

rumint toolhttp://www.rumint.com/software.html

Visual Security Communityhttp://www.ninjabi.net/index.php?option=com_nxtlinks&

catid=41&Itemid=47

Kulsoom’s Researchhttp://users.ece.gatech.edu/~kulsoom/research.html

VizSEC Paper/Slideshttp://users.ece.gatech.edu/~kulsoom/research.html

www.cc.gatech.edu/~conti

Page 28: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Acknowledgements

• Dr. John Stasko– http://www.cc.gatech.edu/~john.stasko/

• Dr. Wenke Lee– http://www.cc.gatech.edu/~wenke/

• Dr. John Levine– http://www.eecs.usma.edu/

• Julian Grizzard– http://www.ece.gatech.edu/

• 404.se2600– Clint– Hendrick– icer– Rockit– StricK

Page 29: Passive Visual Fingerprinting of Network Attack Tools Gregory Conti Kulsoom Abdullah College of Computing Georgia Institute of Technology

Questions?

Image: http://altura.speedera.net/ccimg.catalogcity.com/210000/211700/211780/Products/6203927.jpg

Greg [email protected]/~conti

Kulsoom [email protected]://users.ece.gatech.edu/~kulsoom/research.html