ACM Home Page
Please provide us with feedback. Feedback
ACT: attachment chain tracing scheme for email virus detection and control
Full text PdfPdf (284 KB)
Source Workshop on Rapid Malcode archive
Proceedings of the 2004 ACM workshop on Rapid malcode table of contents
Washington DC, USA
SESSION: Session 1 table of contents
Pages: 11 - 22  
Year of Publication: 2004
ISBN:1-58113-970-5
Author
Jintao Xiong  Universidad del Turabo, Gurabo PR
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): 6,   Downloads (12 Months): 48,   Citation Count: 1
Additional Information:

abstract   references   cited by   index terms  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1029618.1029621
What is a DOI?

ABSTRACT

Modern society is highly dependent on the smooth and safe flow of information over communication and computer networks. Computer viruses and worms pose serious threats to the society by disrupting the normal information flow and collecting or destroying information without authorization. Compared to the effectiveness and ease of spreading worms and viruses, currently adopted defense schemes are slow to react and costly to implement.

This paper proposes an automated email virus detection and control scheme using attachment chain tracing (ACT) technique. Based on conventional epidemiology, ACT detects virus propagation by identifying the existence of transmission chains in the network. It uses contact tracing to find epidemiological links between hosts. A soft quarantine scheme is proposed to control virus propagation. No virus signature information is needed for detection and quarantine. We also study the effect of delayed, limited immunization on the spread of viruses. We propose a progressive immunization strategy which uses transmission chain information to guide immunization process. Preliminary simulation experiments show that ACT is a promising scheme.


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
R. Albert and A. Barabasi. topology of evolving network: local events and universality. physical review letters, 85(24):5234--5237, Dec. 2000.
 
2
CDC. DNA fingerprinting of mycobacterium tuberculosis isolates from epidemiologically linked case pairs.
 
3
CERT. CERT advisory CA -2004-02.
 
4
CERT. CERT incident note IN -2003-03.
 
5
CERT. CERT incident note IN -2004-01.
 
6
CERT/CC. CERT advisory CA -2001-26 nimda worm.
 
7
Z. Chen, L. Gao, and K. Kwiat. Modeling the spread of active worms. In Proc. of IEEE INFOCOM '03, San Francisco, CA, April 2003.
 
8
R. Diel, S. Schneider, K. Meywald, C. Ruf, S. Rusch, and S. Niemann. epidemiology of tuberculosis in hamburg, Germany: long-term population-based analysis applying classical and molecular epidemiological techniques. J. of Clinical Microbiology, 40(2):532--539, Feb. 2002.
 
9
M. Garetto, W. Gong, and D. Towsley. modeling malware spreading dynamics. In IEEE INFOCOM '03, San Francisco, CA, April 2003.
 
10
W. Haas, G. Engelmann, B. Amthor, S. Shyamba, F. Mugala, M. Felten, M. Rabbow, M. Leichsenring, O. Oosthuizen, and H. Bremer. transmission dynamics of tuberculosis in a high-incidence country: prospective analysis by PCR DNA fingerprinting. J. of Clinical Microbiology, 37(12):3975--3979, Dec. 1999.
 
11
 
12
K. Swab. SMTP gateway virus filtering with sendmail and AMaViS.
 
13
J. Kephart and S. White. directed-graph epidemiological models of computer viruses. In Proc. of the 1991 IEEE computer society symposium on research in security and privacy, pages 343--359, May 1991.
 
14
 
15
S. Lockman, J. Sheppard, C. Braden, M. Mwasekaga, C. Woodley, T. Kenyon, N. Binkin, M. Steinman, F. Montsho, M. Kesupile, C. Hirschfeldt, M. Notha, T. Moeti, and J. Tappero. Molecular and conventional epidemiology of mycobacterium tuberculosis in Boswana: a population-based prospective study of 301 pulmonary tuberculosis patients. J. of Clinical Microbiology, 39(3):1042--1047, May 2001.
 
16
17
 
18
D. Moore, C. Shannon, G. Voelker, and S. Savage. Internet quarantine: Requirements for containing self-propagating code. In Proc. IEEE INFOCOM '03, San Francisco, CA, April 2003.
 
19
D. Soolingen. molecular epidemiology of tuberculosis and other mycobacterial infections: main methodologies and achievements. J. Intern. Med., (249):1--26, 2000.
 
20
 
21
T. Toth and C. Kruegel. Connection-history based anomaly detection. In Proc. of the IEEE workshop on information assurance and security, West Point, NY, June 2002.
 
22
23
 
24
WHO. consensus document on the epidemiology of severe acute respiratory syndrome (SARS).
 
25
WHO. SARS: breaking the chains of transmission.
 
26
M. Williamson. Throttling viruses: restricting propagation to defeat malicious mobile code. Technical Report HPL -2002-172, HP laboratories technical report, 2002.
 
27
Y. Zhu, J. Ho, and L. Beauchamp. Email traffic modeling at the access link. Technical report, Nortel networks technical report, 1998.
28
29
30
 
31
C. Zou, D. Towsley, and W. Gong. Email virus propagation modeling and analysis. Technical Report TR - CSE -03-04, University of Massachusetts at Amherst, 2003.