1 / 38

Understanding and Mitigating Spam: Types, Methods, and Network-Level Behavior

This presentation provides an overview of what spam is, statistics on spam volume, different types of spam, various spamming methods used, and mitigation techniques. It also delves into understanding the network-level behavior of spammers, data collection methods, and statistics on spam activity. The presentation concludes with an exploration of BGP spectrum agility, botnets, harvesting, and the drawbacks of spam mitigation approaches.

inouye
Download Presentation

Understanding and Mitigating Spam: Types, Methods, and Network-Level Behavior

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Spam Sagar Vemuri slides courtesy: Anirudh Ramachandran Nick Feamster

  2. Agenda • Understanding Spam • What is Spam? • Statistics • Types of Spam • Spamming Methods • Spam Mitigation Methods • Understanding the Network-level behavior of spammers • Data Collection Methods • Statistics • BGP Spectrum Agility, Botnets, Harvesting • Drawbacks

  3. What is Spam? • Unsolicited commercial message • “Spam is e-mail that is both unsolicited by the recipient and sent in substantively identical form to many recipients” • As of last quarter of 2005, estimates indicate that about 80-85% of all email is spam • Microsoft founder Bill Gates receives four million e-mails per year, most of them being spam

  4. Some statistics • 1978 - An e-mail spam is sent to 600 addresses. • 1994 - First large-scale spam sent to 6000 newsgroups, reaching millions of people • 2005 - (June) 30 billion per day • 2006 - (June) 55 billion per day • 2006 - (December) 85 billion per day • 2007 - (February) 90 billion per day

  5. Products advertised • Porn site subscriptions • Prescription drugs • Printer ink cartridges • Counterfeit software • Mortgage offers • Fake diplomas from non-existent or non-accredited universities

  6. Types of Spam • Email spam • IM spam • Also called ‘Spim’ • 1.2 billion spam IM messages in 2004 • SMS spam • Also called ‘m-spam’ • Image spam • Text of a msg stored as GIF or JPEG and displayed in the email • Prevents text based spam filters from detecting it

  7. 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 • Collection of machines acting under one centralized controller. Eg: Bobax • BGP Spectrum Agility • IP hijacking techniques

  8. Spam Mitigation • Filtering • Based on content • Use features in email’s headers and body • Eg: SpamAssassin • Blacklisting: • IP addresses of known spam sources are used to classify email • More than 30 widely used blacklists available today

  9. Content-based Filtering Content-based properties are malleable • Low cost to evasion:Spammers can easily alter features of an email’s content • Customization: Customized emails are easy to generate • High cost to filter maintainers:Filters must be continually updated as content-changing techniques become more sophisticated • Content-based filters are applied at the destination • Too little, too late:Wasted network bandwidth, storage, etc. Many users receive (and store) the same spam content

  10. DNS Blacklisting • Aggressive filters have many false positives • One list might not have all the information about spamming IPs • Need to consult multiple lists

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

  12. Spamming Patterns Network-level properties of spam arrival • From where? • What IP address space? • ASes? • What OSes? • What techniques? • Botnets • Short-lived route announcements • Shady ISPs • Capabilities and limitations? • Bandwidth • Size of botnet army

  13. Understanding the Network-Level Behavior of Spammers Anirudh Ramachandran Nick Feamster (Georgia Tech)

  14. Data Collection • Primary dataset: Actual spam email messages collected at a large spam sinkhole • Corpus of email logs from a large email provider • Command and Control traffic from a Bobax botnet • BGP route advertisements from an upstream border router in the same network • Also capturing traceroutes, DNSBL results, passive TCP host fingerprinting simultaneous with spam arrival

  15. Data Collection Setup Exchange 1

  16. Data collected when the spam is received • IP address of the relay that established the SMTP connection to the sinkhole • Traceroute 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

  17. MailAvenger • Highly configurable SMTP server that collects many useful statistics

  18. Spam per Day • Both the amount of spam and the number of distinct IP addresses increase over time

  19. IP Address Distribution • The majority of spam is sent from a relatively small fraction of IP address space • The distribution is the same for legitimate mail

  20. AS distribution • Large fraction of spam received from just a handful of ASes • 12% of all received spam originates in just two ASes (from Korea and China) • Top 20 ASes are responsible for sending nearly 37% of all spam • Spam filtering efforts might be better if focussed on identifying high-volume, persistent groups of spammers by AS number rather than on blacklisting individual IP addresses.

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

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

  23. Persistence • More than half of the client IPs appear less than twice • 85% of the client IP addresses sent less than 10 emails to the sinkhole

  24. Effectiveness of Blacklists • Nearly 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 • If spammers use BGP spectrum agility, then 50% of the IP addresses do not appear in any blacklist • About 30% appear in more than one blacklist

  25. Effectiveness of Blacklists

  26. Effectiveness of Blacklists

  27. Spam From Botnets

  28. Most Bot IP addresses do not return Percentage of bots 65% of bots only send mail to a domain once over 18 months Lifetime (seconds) Collaborative spam filtering seems to be helping track bot IP addresses

  29. Most Bots Send Low Volumes of Spam Most bot IP addresses send very little spam, regardless of how long they have been spamming… Amount of Spam Lifetime (seconds)

  30. BGP Spectrum Agility • Log IP addresses of SMTP relays • Correlate BGP route advertisements seen at network where spam trap is co-located. A small club of persistent players appears to be using this technique. Common short-lived prefixes and ASes 61.0.0.0/8 4678 66.0.0.0/8 21562 82.0.0.0/8 8717 ~ 10 minutes Somewhere between 1-10% of all spam (some clearly intentional, others might be flapping)

  31. Why Such Big Prefixes? • Flexibility:Client IPs can be scattered throughout dark space within a large /8 • Same sender usually returns with different IP addresses • Visibility: Route typically won’t be filtered (nice and short)

  32. Characteristics of IP-Agile Senders • IP addresses are widely distributed across the /8 space • IP addresses typically appear only once at the sinkhole • Depending on which /8, 60-80% of these IP addresses were not reachable by traceroute when spot-checked • Some IP addresses were in allocated, albeit unannounced space • Some AS paths associated with the routes contained reserved AS numbers

  33. Length of short-lived BGP epochs

  34. The Effectiveness of Blacklisting ~95% of bots listed in one or more blacklists Fraction of all spam received ~80% listed on average Only about half of the IPs spamming from short-lived BGP are listed in any blacklist Number of DNSBLs listing this spammer Spam from IP-agile senders tend to be listed in fewer blacklists

  35. Harvesting • Tracking Web-based harvesting • Register domain, set up MX record • Post, link to page with randomly generated email addresses • Log requests • Wait for spam

  36. Harvesting • Domain was registered on November 19, 2005 • SMTP server was setup on December 6, 2005 • Email harvesting occurred on January 16, 2006 • First spam came on January 20, 2006 (phishing attack) • The harvester and the spammers were not in the same AS • Attack was coordinated between two machines • One machine sent to half of the addresses listed alphabetically, the other machine to the other half

  37. Spam Mitigation • Spam filtering requires a better notion of host identity • IP address is not enough to identify an host • IP address range based filtering is more effective than single IP address based filtering • Some IP address ranges send more spam than others • Securing the Internet routing is necessary for bolstering identity and traceability of email senders • BGP spectrum agility method can be used more • Network-level properties can make current spam filters more effective

  38. Conclusion • A detailed study examining network level properties • Reveals botnet characteristics in sending spam • Shows the existence of BGP spectrum agility method • Datasets are substantial, but not comprehensive • Comparison between spam and legitimate mail is questionable • Comparison between spam and legitimate mail of a single domain, repeating this using several domains can be better? • Analysis of IP addresses and address ranges fails to draw important conclusions • Does not analyze other types of spam, apart from email spam. • Data Analysis from a single vantage point

More Related