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Understanding the network level behavior of spammers

Understanding the network level behavior of spammers. Published by :Anirudh Ramachandran, Nick Feamster Published in :ACMSIGCOMM 2006 Presented by: Bharat Soundararajan. OUTLINE. Spam - Basics of spam - Spam statistics - Spamming methods

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Understanding the network level behavior of spammers

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  1. Understanding the network level behavior of spammers Published by :Anirudh Ramachandran, Nick Feamster Published in :ACMSIGCOMM 2006 Presented by: Bharat Soundararajan

  2. OUTLINE • Spam - Basics of spam - Spam statistics - Spamming methods - Spam filtering • Network level behavior of spam - Network level spam filtering - Data Collection Method - Tools used for data collection - Evaluations - Drawbacks

  3. SPAM

  4. What is Spam? • E-mail spam, also known as "bulk e-mail" or "junk e-mail," is a subset of spam that involves nearly identical messages sent to numerous recipients by e-mail. • Spammers use unsecured mail servers to send out millions of illegitimate emails • 2007 - (February) 90 billion per day

  5. Spam statistics

  6. Spamming Methods • Direct spamming • By purchasing upstream connectivity from “spam-friendly ISPs” • Open relays and proxies • Mail servers that allow unauthenticated Internet hosts to connect and relay mail through them • Botnets Using the worm to infect mail servers and sending mail through them e.g.bobax • BGP Spectrum Agility Short lived BGP route announcements

  7. Botnet command and control • Already captured Command and control center information • is used for the sinkhole to act like command and control • center • All bots now try to contact the command and control • sinkhole and they collected a packet trace to determine the • members of botnet • They observed a significantly higher percentage of infected • hosts is windows using Pof passive fingerprinting tool • Information collected is not accurate

  8. Sink hole

  9. Dns blacklisting • A list of open-relay mail servers or open proxies—or of • IP addresses known to send spam • Data collected from Spam-trap addresses or • honeypots • 80% of all spam received from mail relays • appear in at least one of eight blacklists • > 50% of spam was listed in two or more • blacklists

  10. Spam filtering • Spammers are able to easily alter the • contents of the email • SpamAssasin : a spam filter used for filtering • is mainly source Ip and other variables • which is easily changed by spammers • They have less flexibility when comes to • altering the network level details of email

  11. Spam filtering by this paper - Comparing data with the logs from a large ISP - Analyzing the network level behavior using those logs in the sinkhole - Update the filter content using those comparison

  12. Network-level Spam Filtering • Network-level properties are harder to change than content • Network-level properties • IP addresses and IP address ranges • Change of addresses over time • Distribution according to operating system, country and AS • Characteristics of botnets and short-lived route announcements • Help develop better spam filters

  13. Data collected when the spam is received • IP address of the mail relay • Trace route to that IP address, to help us estimate the network location of the mail relay • Passive “p0f” TCP fingerprint, to determine the OS of the mail relay • Result of DNS blacklist (DNSBL) lookups for that mail relay at eight different DNSBLs

  14. Mail avenger • few of the environment variables Mail Avenger sets • CLIENT_NETPATH the network route to the client • SENDER the sender address of the message • CLIENT_SYNOS a guess of the client's operating system type

  15. Distribution across ASes Still about 40% of spam coming from the U.S.

  16. Pof fingerprinting • Passive Fingerprinting is a method to learn more about the • enemy, without them knowing it • Specifically, you can determine the operating system and other • characteristics of the remote host • TTL – what TTL is used for the operating system • Window Size – what window size the operating system uses • DF – whether the operating system set the don’t fragment bit • TOS – Did the operating system specify what type of service

  17. OS guess from ttl values

  18. Distribution Among Operating Systems About 4% of known hosts are non-Windows. These hosts are responsible for about 8% of received spam.

  19. Spam Distribution IP Space

  20. Advantages • A key to better and efficient filtering • Reporting of information about spam helps in updating the blacklist

  21. Weaknesses • They cannot distinguish between spam obtained from different techniques • They didn’t precisely measure using bobax botnet

  22. THANK YOU

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