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Matching TCP/IP Packet to
Detect Stepping-stone Intrusion
Jianhua Yang
TSYS School of Computer Science
Edward Bosworth
Center for Information Assurance Education
Columbus State University
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Layout
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Background
Related Work
SWAM algorithm
Compare with SDC
Conclusion and future work
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1. Background
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How to attack other computers?
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Interactive
Non-interactive
Interactive attack
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Direct
Indirect
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Indirect attack
Stepping-stone
Intrusion
Stepping-stone
Intrusion
Detection
Victim
Attacker
Stepping-stones
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Monitor Point
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A detection model
Incoming
Connection
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Outgoing
Connection
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2. Related Work
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Content-based (Thumbprint) [1]
Time-based (ON-OFF)[2]
Deviation-based[3]
Packet number based [4,7]
Watermark-based [5,6]
One dimension Random-Walk [Yang-13]
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Another model
Send-Echo
Steppingstone
Send-Ack
Ratio=RTT (Send_Ack) / RTT(Send-Echo)
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The problems
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Length estimation
Measure bar
Absorbing
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Matching TCP Packet
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Step-function (Packet-matching)[8-yang]
Fluctuation estimation [9-yang]
Clustering-Partitioning algorithm [10-yang, 11-yang]
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SDC
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(Standard deviation based Cluster Matching)
RTT distribution
Figure 1: A distribution of RTT for a connection chain
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How SDC works
S={s1, s2, s3, s4}
={1099702684, 1099772525, 1099909440, 1099928524}
E={e1, e2, e3, e4}
={1099828523, 1099898019, 1100036000, 1100058999 }
S1={125839, 195335, 333316, 356315},
S2={55998, 125494, 263475, 286474},
S3={-80917, -11421, 126560, 149559},
S4={-100001, -30505, 107476, 130475}.
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Basic Idea to do SDC
S={s1, s2, …, sn}
E={e1, e2, …, em}
S1={s1e1, s1e2,…, s1em},
S2={s2e1, s2e2,…, s2em},
…
Sn={sne1, sne2,…, snem }.
Combination
Clusters
Standard Deviation
Computing
Get the smallest one
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complexity
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mn
Example:
 80 send packets
 115 echo packets
 11580 =7.175e+164 clusters
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SWAM
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(sliding window packet matching algorithm)
S = {s1, s2, s3, s4, s5, s6, s7, s8, s9, s10}
E = {e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13, e14}
Window size =3
Q= {s1, s2, e1, s3, e2, s4, e3, e4, s5, e5, s6, e6, e7, s7, e8, e9, s8, e10, s9, e11, e12,
s10, e13, e14}
Q1= {s1, s2, e1, s3, e2, s4, e3, e4, s5, e5, s6, e6, e7, s7, e8,
e9, s8, e10, s9, e11, e12, s10, e13, e14}
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Comparison
For the previous example
SDC: number of clusters =
1410 = 289254654976
SWAM: number of clusters
= 210 = 1024
0.00000035%
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General Comparison
i  w2
1
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n
i  w1
m
n
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Live Sliding Window
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Why use LSW?
Possible?
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How to use LSW?
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Determine the size of SLW by
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Gap between si and sj
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Why SWAM works?
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Six facts from TCP/IP protocol
For details, please read the paper Section
3.1 Motivation.
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Conclusion
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SWAM works and more efficient than
SDC in terms of Matching TCP/IP
packets.
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Future work
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Using SWAM to compute the length of a
connection chain.
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References
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[1] Staniford-Chen, S., and Todd Heberlein, L.: Holding Intruders Accountable on the Internet. Proc. IEEE Symposium on
Security and Privacy, Oakland, CA, USA (1995) 39-49.
[2] [YZ00] Zhang, Y., and Paxson, V.: Detecting Stepping Stones. Proc. of the 9th USENIX Security Symposium, Denver, CO,
USA (2000) 171-184.
[3] Yoda, K., and Etoh, H.: Finding Connection Chain for Tracing Intruders. Proc. 6th European Symposium on Research in
Computer Security, Toulouse, France (2000) 31-42.
[4] Blum, A., Song, D., and Venkataraman, S.: Detection of Interactive Stepping-Stones: Algorithms and Confidence Bounds.
Proceedings of International Symposium on Recent Advance in Intrusion Detection (RAID), Sophia Antipolis, France (2004) 2035.
[5] X. Wang, D. S. Reeves, S. F. Wu, and J. Yuill, “Sleepy Watermark Tracing: An Active Network-based Intrusion Response
Framework,” Proceedings of 16th International Conference on Information Security, Paris, France, June 2001, pp. 369-384.
[6] X. Wang, D. Reeves, and S. Wu, “Inter-Packet Delay-based Correlation for Tracing Encrypted Connections through
Stepping Stones,” Proceedings of 7th European Symposium on Research in Computer Security, Lecture Notes in Computer
Science. Zurich, Switzerland, October 2002, Vol. 2502, pp. 244-263.
[7] T. He and L. Tong, “Detecting Encrypted Interactive Stepping-Stone Connections,” Proc. 2006 IEEE International
Conference on Acoustics, Speech, and Signal Processing, Toulouse, France, May 2006.
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Cont.
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[8] Jianhua Yang, Shou-Hsuan Stephen Huang, "A Real-Time Algorithm to Detect Long Connection Chains of Interactive
Terminal Sessions," Proceedings of 3rd ACM International Conference on Information Security (Infosecu'04), Shanghai, China,
November 2004, pp. 198-203. (Accepting rate=25%)
[9] Jianhua Yang, Shou-Hsuan Stephen Huang, "Charactering and Estimating Network Fluctuation for Detecting Interactive
Stepping-Stone Intrusion," the Proceedings of International Conference on Communication, Network and Information Security,
Phoenix, Arizona, November 2005, pp. 70-75. (Accepting rate=34%).
[10] Jianhua Yang, Shou-Hsuan Stephen Huang, Ming D. Wan, "A Clustering-Partitioning Algorithm to Find TCP Packet
Round-Trip Time for Intrusion Detection," Proceedings of 20th IEEE International Conference on Advanced Information
Networking and Applications (AINA 2006), Vienna, Austria, April 2006, Vol. 1, pp 231-236.(Accepting rate=30%).
[11] Jianhua Yang, Stephen Huang, “Probabilistic Analysis of an Algorithm to Compute TCP Packet Round-Trip Time for
Intrusion Detection”, Journal of Computers and Security, Elsevier Ltd., pp 137-144, Vol. 26 (2007).
[12] Guoqing Zhao, Jianhua Yang, Long Ni, Gurdeep S. Hura, and Shou-Hsuan Stephen Huang, "Correlating TCP/IP Interactive
Sessions with Correlation Coefficient to Detect Stepping-Stone Intrusion," to be published in the Proceedings of 23nd IEEE
International Conference on Advanced Information Networking and Applications (AINA 2009), Bradford, UK, May 2009.
[13] Jianhua Yang, Byong Lee, Shou-Hsuan Stephen Huang, "Monitoring Network Traffic to Detect Stepping-Stone Intrusion,"
the Proceedings of 22nd IEEE International Conference on Advanced Information Networking and Applications (AINA 2008),
Okinawa, Japan, pp 56-61 March 2008.
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Thanks!
 Questions?
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