@techreport{TD:100313,
	att_abstract={{Firewalls are critical security devices handling all traffic in and out of a network. Firewalls, like other software and hardware network devices, have vulnerabilities, which can be exploited by motivated attackers. However, because firewalls are usually placed in the network such that they are transparent to the end users, it is very difficult to identify them and use their corresponding vulnerabilities to attack them. In this paper, we study firewall fingerprinting, in which one can use firewall decisions on a sequence of TCP packets with unusual flags and machine learning techniques for inferring firewall implementation.}},
	att_authors={dp8327, zg2325, jw2129},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE INFOCOM 2012. {{, 2012-03-22}}

(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM  SIGmetrics 2011 {{, 2012-03-22}}.}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100313},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100313_DS1_2011-02-14T22:20:18.178Z.pdf},
	author={Dan Pei and Zihui Ge and Jia Wang and Amir R. Khakpour (Michigan State University) and Josh Hulst (Michigan State University) and Alex X. Liu (Michigan State University)},
	institution={{IEEE INFOCOM 2012}},
	month={March},
	title={{Firewall Fingerprinting}},
	year=2012,
}