ACM Home Page
Please provide us with feedback. Feedback
Payload attribution via hierarchical bloom filters
Full text PdfPdf (391 KB)
Source Conference on Computer and Communications Security archive
Proceedings of the 11th ACM conference on Computer and communications security table of contents
Washington DC, USA
SESSION: Network intrusions table of contents
Pages: 31 - 41  
Year of Publication: 2004
ISBN:1-58113-961-6
Authors
Kulesh Shanmugasundaram  Polytechnic University
Hervé Brönnimann  Polytechnic University
Nasir Memon  Polytechnic University
Sponsors
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 11,   Downloads (12 Months): 110,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1030083.1030089
What is a DOI?

ABSTRACT

Payload attribution is an important problem often encountered in network forensics. Given an excerpt of a payload, finding its source and destination is useful for many security applications such as identifying sources and victims of a worm or virus. Although IP traceback techniques have been proposed in the literature, these techniques cannot help when we do not have the entire packet or when we only have an excerpt of the payload.

In this paper, we present a payload attribution system (PAS) that attributes reasonably long excerpts of payloads to their source and/or destination hosts. The system we propose is based on a novel data structure called a Hierarchical Bloom Filter (HBF). An HBF creates compact digests of payloads and provides probabilistic answers to membership queries on the excerpts of payloads. We also present the performance analysis of the method and experimental results from a prototype demonstrating the practicality and efficacy of the system. The system can reliably work with certain packet transformations and is flexible enough to be used if the query string is spread across several packets. The system, however, can be evaded by splitting or by "stuffing" the payload. Future work focuses on making the system robust against such evasions.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Infinistream. http://www.networkgeneral.com/.
 
2
Snort. http://www.snort.org/.
 
3
 
4
S. M. Bellovin, M. Leech, and T. Taylor. ICMP traceback messages. In Internet Draft draft-ietf-itrace-01.txt (Work in progress). IETF, Oct 2001.
5
 
6
A. Broder and M. Mitzenmatcher. Network applications of bloom filters: A survey. In Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, Illinois, USA, October 2002.
 
7
H. Brönnimann, K. Shanmugasundaram, and N. Memon. String matching on the internet. In Workshop on Combinatorial and Algorithmic Aspects of Networking, Banf, Canada, August 2004.
 
8
 
9
CERT. Cert incident note in-2004-1. ttp://www.cert.org/incident_notes/IN-2004-01.html.
 
10
D. Dean, M. Franklin, and A. Stubblefield. An algebraic approach to IP traceback. In Proceedings of NDSS, Feb 2001.
 
11
S. Dharmapurikar, M. Attig, and J. Lockwood. Design and implementation of a string matching system for network intrusion detection using FPGA-based bloom filters. Technical Report, CSE Dept, Washington University, 2004. Saint Louis, MO.
 
12
Sandstorm Enterprises. NetIntercept. http://www.sandstorm.com/.
13
 
14
I. Hamadeh and G. Kesidis. Packet marking for traceback of illegal content distribution. In Proceedings of International Conference on Cross-Media Service Delivery (CMSD), Santorini, Greece, May 2003.
 
15
I. Hamadeh and G. Kesidis. Performance of ip address fragmentation strategies for ddos traceback. In Proceedings of IEEE IPCOM, Kansas City, October 2003.
 
16
Abhishek Kumar, Jun Xu, Jia Wang, Oliver Spatschek, and Li Li. Space-code bloom filter for efficient per-flow traffic measurement. In Proceedings of IEEE INFOCOM, Hong Kong, China, March 2004.
 
17
S. Kumar and E. H. Spafford. An application of pattern matching in intrusion detection. Purdue University Technical Report CSD-TR-94-013, 1994.
 
18
A. Mankin, D. Massey, C. L. Wu, S. F. Wu, and L. Zhang. On design and evaluation of "intention-driven" ICMP traceback. In Proc. IEEE International Conference on Computer Communications and Networks, Oct 2001.
 
19
S. McCreary and K. Claffy. Trends in wide area ip traffic patterns: A view from ames internet exchange. In ITC Specialist Seminar on IP Traffic Modelling, Measurement, and Management, March 2000.
 
20
21
 
22
V. Paxson. Bro: A system for detecting network intruders in real-time. 7th Annual USENIX Security Symposium, January 1998.
 
23
T. H. Ptacek and T. N. Newsham. Insertion, evasion, and denial of service: Eluding network intrusion detection. Secure Networks, Inc., January 1998.
24
25
 
26
K. Shanmugasundaram, A. Savant, H. Brünnimann, and N. Memon. Fornet: A distributed forensics network. In The Second International Workshop on Mathematical Methods, Models and Architectures for Computer Networks Security, St. Petersburg, Russia, October 2003.
27
 
28
D. Song and A. Perrig. Advanced and authenticated marking schemes for IP traceback. In IEEE Infocomm, 2001.
 
29
 
30
 
31
Y. Zhang and V. Paxson. Detecting stepping stones. In Proceedings of the 9th USENIX Security Symposium, Denver, Colorado, USA, August 2000.


Collaborative Colleagues:
Kulesh Shanmugasundaram: colleagues
Hervé Brönnimann: colleagues
Nasir Memon: colleagues