report on “spamming botnets: signatures and characteristics ” heyong wang department of computer...
TRANSCRIPT
Report on “Spamming Botnets: Signatures and
Characteristics ”
Heyong Wang
Department of Computer Science
Iowa State University
Outline
• Background
• Related study and their limitations
• Proposed solution
• Experimental result and evaluation
• Discussion
New World, New War!
• Internet has greatly shaped our sociality
• Increasing challenges: Internet Security!
Myth of Internet: Attack vs. Defense
Introduction-botnet
• What is botnet?
A group of compromised host computers that are controlled by a small number of commander hosts refer as Command and Control (C&C) server. Botnets have been widely used for networks attacks and spam emails sending at a large scale.
Botnet: one of top threats
• Stealing data
• Hosting fraudulent Web sites
• Participating in DoS (denial of service) attacks
• Sending spam emails….
Is the Botnet Battle Already Lost?
• According to statistics released by Symantec, an average of 57,000 active bots was observed per day over the first six months of 2006 [1]
• "Bots are at the center of the undernet economy," says Jeremy Linden, until recently a researcher at Arbor Networks
• Networks of bots distribute as much as 90 percent of all junk email, says David Dagon, a doctoral student at Georgia Tech who wrote his thesis on the topic
Is the Botnet Battle Already Lost?
• According to SecureWorks, 20.6 million attacks originated from U.S. computers and 7.7 million from Chinese computers [2]
• World: 6.23 million bot-infected computers on the Internet in 2007 [3]
• China: 3.62 million in China’s address space in 2007 [3]
The goals of this paper
• Perform a large scale analysis of spamming botnet characteristics
• Identify spam botnet activity trends
• Study future botnet detection and defense mechanism
How it works: an example
IRC : Internet Relay Chat
Existing related work
• The botnet infection and their associated control process have been studied and analyzed in [4, 5, 6]
• Ramachandran el al. [7] perform a study of network behavior of large scale spammers, providing strong evidence that botnets are commonly used as platforms for sending spam.
• Ramachandran el al proposed a way to infer membership and identify spammers by monitoring queries to DNSBL and by clustering email servers based on their target email destination domains[8]
Existing related work
• Zhuang et al. showed that the similarity of email texts can help identify botnet-based spam campaigns [8].
• Li and Hsish found that spam emails with identical campaigns are highly clusterable and are often sent in a burst [9].
• The spam URL signatures generation problem is in many ways similar to the content-based worm signature generation problem that have been extensively studied [10, 11, 12, 13, 14].
However
• how to correctly group those spam emails based on the campaigns has not yet discussed and studied
• There are two challenges remaining to prevent directly adopting existing solutions for botnet spam signature generation spammers add legitimate URLs to increase the
perceived legitimacy of emails spammers extensively use URL obfuscation
techniques to evade detection
Proposed solution: AutoRE
• AutoRE: a signature generation framework Detect botnet-based spam emails and botnet membe
rship Does not require pre-classified training data Output high quality regular expression signatures
• AutoRE contains three components: A URL preprocessor A Group selector A RegEx (Regular Expression) generator
AutoRE working mechanism
AutoRE Modules and processing flow chart
Algorithmic overview of generating polymorphic URL signature
URL Pre-Processing
• Extracts information from given emails: URL string Source IP address Email Sending time
• Assign a unique ID to the extracted email• URL preprocessor partitions URLs into groups based on domain: Spam tends to advertise the same product or service from the same domain!
URL Group Selection
• Email might be associated with multiple groups Email contains multiple URLs pertaining
to different domains
• Group selector selects URL group if it is: “bursty”: exhibits the strongest temporal corre
lation “distributed”:Across a large set of distributed
senders
Signature Generation andBotnet Identification• RegEx generates two types of signatures :
complete URL based signatures -- detect spam contains an identical URL regular expression signatures -- detect spam contains polymorphic URLs
• Botnet Identification must satisfy: “distributed”: quantified by the total number of Autono
mous Systems (ASes) “bursty”: quantified using the inferred duration of a bot
net spam campaign “specific”: quantified using an information entropy met
ric
Automatic URL Regular Expression Generation • Keyword based signature tree construction• Candidate regular expressions generation
Detailing: returns a domain specific regular expression using a keyword-based signature as input Generalization: returns a more general domain- agnostic regular expression by merging very similar domain-specific regular expressions
• Ensure generated expression are specific enough Measure the quality of a signature Discard that are too general
Example: input URLs and the keyword-based signature tree construed by AutoRE
Generalization: Merging domain-specific regular expressions into domai
n-agnostic regular expression
Result and evaluation
• Dataset: Randomly Sampled Hotmail emails in Nov
06, Jun 07, July 07 Senders’ IP were not blacklisted Number: 5,382,460 (sampling rate1:25000)
Result and evaluation con’t
CU: complete URL based signatures RE: regular expression signatures
Result and evaluation con’t
• False positive rate (FPR): CU: 0.0001 to 0.0006, RE: 0.0011 to 0.0014
• Ability to detect future spam URL signature detected 16% to 18% of spam RE signature much more robust for future detection
• Regular Expression vs. Keyword Conjunction RE reduce FPR by a factor of 10 to 30
• Domain-Specific vs. Domain-Agnostic signatureDA detect 9.9-20.6% more spam
Discussion: Limitations and some thoughts on proposed solution• Sampling rate (1: 25000) is insufficient to p
erform real-time experiments
• Dataset was only from the Hotmail, result may not be applied to other email servers
• May not work well if the spammer using URLs redirection techniques
• Spammers may attempt to craft emails to evade the AutoRE URL selection process
Thank you
谢谢
References:[1] http://www.eweek.com/c/a/Security/Is-the-Botnet-Battle-Already-Lost/[2]http://www.gcn.com/online/vol1_no1/47200-1.html[3] http://www.securityfocus.com/brief/827[4] K. Chiang and L. Lloyd. A case study of the Rustock rootkit and spam bot. I
n The First Workshop in Understanding Botnets, 2007.[5] M. A. Rajab, J. Zarfoss, F. Monrose, and A. Terzis. A multifaceted approac
h to understanding the botnet phenomenon. In IMC ’06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement, 2006.
[6] N. Daswani, M. Stoppelman, and the Google click quality and security teams. The anatomy of Clickbot.A. In The First Workshop in Understanding Botnets, 2007.
[7] A. Ramachandran and N. Feamster. Understanding the network-level behavior of spammers. In Proceedings of Sigcomm, 2006.
[8] A. Ramachandran, N. Feamster, and S. Vempala. Filtering spam with behavioral blacklisting. In Proceedings of the 14th ACM conference on computer and communications security, 2007.
References:[9] L. Zhuang, J. Dunagan, D. R. Simon, H. J. Wang, I. Osipkov, G. Hulten, and J.
Tygar. Characterizing botnets from email spam records. In LEET 08: First USENIX Workshop on Large-Scale Exploits and Emergent Threats, 2008.
[10] F. Li and M.-H. Hsieh. An empirical study of clustering behavior of spammers and group-based anti-spam strategies. In CEAS 2006: Proceedings of the 3rd conference on email and anti-spam, 2006.
[11] S. Singh, C. Estan, G. Varghese, and S. Savage. Automated worm fingerprinting. In OSDI, 2004.
[12] H.-A. Kim and B. Karp. Autograph: Toward automated, distributed worm signature detection. In the 13th conference on USENIX Security Symposium, 2004.
[13] J. Newsome, B. Karp, and D. Song. Polygraph: Automatically generating signatures for polymorphic worms. In Proceedings of the 2005 IEEE Symposium on Security and Privacy, 2005.
[14] J. Newsome, B. Karp, and D. Song. Polygraph: Automatically generating signatures for polymorphic worms. In Proceedings of the 2005 IEEE Symposium on Security and Privacy, 2005.
[15] C. Kreibich and J. Crowcroft. Honeycomb: Creating intrusion detection signatures using honeypots. In 2nd Workshop on Hot Topics in Networks (HotNets-II), 2003.