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An effective defense against email spam laundering
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Source Conference on Computer and Communications Security archive
Proceedings of the 13th ACM conference on Computer and communications security table of contents
Alexandria, Virginia, USA
SESSION: Privacy and authentication table of contents
Pages: 179 - 190  
Year of Publication: 2006
ISBN:1-59593-518-5
Authors
Mengjun Xie  The College of William and Mary, Williamsburg, VA
Heng Yin  The College of William and Mary, Williamsburg, VA
Haining Wang  The College of William and Mary, Williamsburg, VA
Sponsors
SIGSAC: ACM Special Interest Group on Security, Audit, and Control
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Laundering email spam through open-proxies or compromised PCs is a widely-used trick to conceal real spam sources and reduce spamming cost in underground email spam industry. Spammers have been plaguing the Internet by exploiting a large number of spam proxies. The facility of breaking spam laundering and deterring spamming activities close to their sources, which would greatly benefit not only email users but also victim ISPs, is in great demand but still missing. In this paper, we reveal one salient characteristic of proxy-based spamming activities, namely packet symmetry, by analyzing protocol semantics and timing causality. Based on the packet symmetry exhibited in spam laundering, we propose a simple and effective technique, DBSpam, to on-line detect and break spam laundering activities inside a customer network. Monitoring the bi-directional traffic passing through a network gateway, DBSpam utilizes a simple statistical method, Sequential Probability Ratio Test, to detect the occurrence of spam laundering in a timely manner. To balance the goals of promptness and accuracy, we introduce a noise-reduction technique in DBSpam, after which the laundering path can be identified more accurately. Then, DBSpam activates its spam suppressing mechanism to break the spam laundering. We implement a prototype of DBSpam based on libpcap, and validate its efficacy through both theoretical analyses and trace-based experiments.


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
 
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3
 
4
 
5
 
6
 
7
Composite blocking list. http://cbl.abuseat.org.
 
8
Domainkeys: Proving and protecting email sender identity. http://antispam.yahoo.com/domainkeys.
 
9
 
10
MTA Authorization Records in DNS. http://www.ietf.org/html.charters/OLD/marid-charter.html.
 
11
The penny black project. http://www.research.microsoft.com/research/sv/PennyBlack/.
 
12
SPF. http://www.openspf.org.
 
13
M. Andreolini, A. Bulgarelli, M. Colajanni, and F. Mazzoni. Honeyspam: Honeypots fighting spam at the source. In Proc. USENIX SRUTI 2005, Cambridge, MA, July 2005.
 
14
A. Back. Hashcash. http://www.hashcash.org/.
 
15
 
16
A. Blum, D. X. Song, and S. Venkataraman. Detection of interactive stepping stones: Algorithms and confidence bounds. In Proc. RAID 2004, Sophia Antipolis, France, September 2004.
 
17
L. Donnerhacke. Teergrubing faq. http://www.iks-jena.de/mitarb/lutz/usenet/teergrube.en.html.
 
18
S. Garriss, M. Kaminsky, M. J. Freedman, B. Karp, D. Mazieres, and H. Yu. Re: Reliable email.
 
19
P. Graham. A plan for spam. http://www.paulgraham.com/spam.html.
 
20
 
21
J. Jung, V. Paxson, A. W. Berger, and H. Balakrishnan. Fast portscan detection using sequential hypothesis testing. In Proc. IEEE Symposium on Security and Privacy 2004, Oakland, CA, May 2004.
22
 
23
B. Krishnamurthy and E. Blackmond. SHRED: Spam harassment reduction via economic disincentives. http://www.research.att.com/ bala/papers/shred-ext.pdf.
24
 
25
N. Provos. A virtual honeypot framework. In Proc. USENIX Security 2004, San Diego, CA, August 2004.
26
 
27
A. Ramachandran, D. Dagon, and N. Feamster. Can DNS-based blacklists keep up with bots? In CEAS 2006, Mountain View, CA, July 2006.
28
 
29
 
30
R. D. Twining, M. M. Williamson, M. Mowbray, and M. Rahmouni. Email prioritization: Reducing delays on legitimate mail caused by junk mail. In Proc. USENIX Annual Technical Conference 2004, Boston, MA, June 2004.
 
31
A. Wald. Sequential Analysis. Dover Publications, 2004.
 
32
M. Walfish, J. Zamfirescu, H. Balakrishnan, D. Karger, and S. Shenker. Distributed quota enforcement for spam control. In Proc. USENIX NSDI 2006, San Jose, CA, May 2006.
 
33
 
34
D. Woolridge, J. Law, and M. Kawasaki. The qmail spam throttle mechanism. http://spamthrottle.qmail.ca/man/qmail-spamthrottle.5.html.
 
35
B. Yerazunis. Crm114 - the controllable regex mutilator. http://crm114.sourceforge.net.
 
36
Y. Zhang and V. Paxson. Detecting stepping stones. In Proc. USENIX Security 2000, Denver, CO, August 2000.


Collaborative Colleagues:
Mengjun Xie: colleagues
Heng Yin: colleagues
Haining Wang: colleagues