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 - Spam filtering  Network level behavior of spam - Network level spam filtering - Data Collection Method - Tools used for data collection - Evaluations - Drawbacks 2

3 SPAM

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

Spam statistics 5

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 6

Botnet command and control 7  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

Sink hole 8

Dns blacklisting 9  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

Spam filtering 10  Spammers are able to easily alter the contents of the  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

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 11

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 12

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 13

Mail avenger 14  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

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

Pof fingerprinting 16  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

OS guess from ttl values 17 OPERATING SYSTEM VERSION TTL VALUES LINUX Red Hat 9 64 FREE BSD Solaris2.5.1,2.6,2.7, Windows98 32 windows XP 128

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

Spam Distribution 19 IP Space

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

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

22 THANK YOU