slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Anti-Spam Strategies PowerPoint Presentation
Download Presentation
Anti-Spam Strategies

Loading in 2 Seconds...

play fullscreen
1 / 9

Anti-Spam Strategies - PowerPoint PPT Presentation


  • 235 Views
  • Uploaded on

Anti-Spam Strategies Joshua Alspector AOL Seeds of Spam What is spam? Unsolicited bulk e-mail? Anything you didn’t ask for? - kill direct marketing Personal definition? - affects policies, filtering Libertarian roots of Internet Free speech by anyone to anyone

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Anti-Spam Strategies' - Olivia


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Anti-Spam Strategies

Joshua Alspector

AOL

FDIS, August 1, 2003

slide2

Seeds of Spam

  • What is spam?
    • Unsolicited bulk e-mail?
    • Anything you didn’t ask for? - kill direct marketing
    • Personal definition? - affects policies, filtering
  • Libertarian roots of Internet
    • Free speech by anyone to anyone
    • Trustful protocols like SMTP
    • Anonymous: no checking IDs
  • Scale of e-mail
    • Cheaper and easier than postal mail
    • As easy to send 1 message as 1 million
    • Costs borne by recipient not sender

FDIS, August 1, 2003

slide3

Ecology of Spam

  • Low cost to sender
    • Spammers make money with only 1 in 100,000 response
    • No incremental costs for bulk mail
    • Commission system for spammers
  • High cost to business
    • $10B/yr in productivity, processing, anti-spam tools
    • Reduces usefulness of email
    • Other messages (IM, chat) affected as well
  • Spam-a-lot
    • Can register for free accounts automatically
    • Can hijack relays, proxies
    • Can obscure IP addresses
    • Can script mail easily

FDIS, August 1, 2003

slide4

Blocking Spam

  • Blacklists
    • Mail, IP addresses from complaints
    • Operations likes this, keeps system costs down
      • Collateral damage, direct marketers hate this
  • Whitelists
    • Buddies, address book, ‘people I know’ , auto-populate
      • Special marketing arrangements – a problem
  • Filters
    • Keywords, adaptive, high-volume signatures
      • Weapon of choice but must avoid collateral damage
  • Challenge-Response
    • First time mailer must fill in human-readable form
      • Rude, problem with receipts, alerts, listservs

FDIS, August 1, 2003

slide5

Text Spam Filters

  • Bayesian filters
    • Popular, See "Better Bayesian Filtering“

 http://paulgraham.com/better.html (Jan, 2003).

    • Easy to store word counts and calculate probabilities
  • Adaptive, content-based technique
    • Content is what spammers can’t hide
    • Adapt as fast as spammers
  • Algorithms considered
    • Naive Bayes
    • Support Vector Machine
    • Perceptron

FDIS, August 1, 2003

slide6

Arms Race

  • Adaptive filters learn what you consider spam
    • Spammers adjust (e.g. v’i’a’g’r’a, graphical, html tables)
    • Driven to deceptive subject lines, images, hijacked accounts
    • Check drop boxes to see what gets through
    • More sophisticated clients
      • Picture signatures, unicode, vector graphics
    • Must learn to see in ‘eye space’
  • Volume filters
    • Append random text to fool signature techniques
    • Chop up mailings in small chunks
    • Hijack open proxies, multiple ISPs
  • Scripted automatic free mail registrations
    • ISPs recently implemented Turing-type challenge

FDIS, August 1, 2003

slide7

Direct Marketing Problems

  • ISPs allow bulk mail from clients
    • Significant complaints from this ‘whitelisted’ mail
    • Spam looks almost identical to adaptive filters
  • Direct Marketers’ Position
    • Would like to avoid spam blocks
      • Honest subject and headers
      • Opt-out mechanism
      • Seal of integrity or consent token
  • Legal Approach
    • Laws against deceptive advertising
    • People love idea of ‘do not spam’ list
      • 90% of spam is untraceable to original sender
      • Much comes from Korea, China, Pakistan, Colombia, Russia, Japan

FDIS, August 1, 2003

slide8

Other Strategies

  • Economic
    • Transfer cost to sender
      • E.g. First 100/day free, then $.001 next 1000, $.01 next 100,000
      • E.g. Make senders post bond which receivers can collect
      • E.g. Make senders perform a compute intensive task (encrypt?)
      • Much spam comes from unsuspecting victims of hijacked accounts
  • Authentication
    • Strip email of anonymity, trace like phone calls (SS7?)
    • Authenticate with encrypted tokens
      • 3rd party anonymizer
      • Unique digital stamp for each email
    • Reputation mechanism or trust seal
    • Need to re-engineer e-mail and SMTP

FDIS, August 1, 2003

slide9

Is Tide Turning?

  • From Spam-a-lot
  • To Spam-a-geddon?
  • Opinions? Ideas?

FDIS, August 1, 2003