Against Spam Methodologies.

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Category: Medical / Health
10B/yr in efficiency, preparing, hostile to spam apparatuses. Lessens value of email. Other ... Much spam originates from clueless casualties of captured records ...
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Against Spam Strategies Joshua Alspector AOL FDIS, August 1, 2003

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Seeds of Spam What is spam? Spontaneous mass email? Anything you didn\'t request? - murder direct showcasing Personal definition? - influences approaches, separating Libertarian foundations of Internet Free discourse by anybody to anybody Trustful conventions like SMTP Anonymous: no checking IDs Scale of email Cheaper and less demanding than postal mail As simple to send 1 message as 1 million Costs borne by beneficiary not sender FDIS, August 1, 2003

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Ecology of Spam Low cost to sender Spammers profit with just 1 in 100,000 reaction No incremental expenses for mass mail Commission framework for spammers High cost to business $10B/yr in efficiency, preparing, hostile to spam instruments Reduces handiness of email Other messages (IM, visit) influenced too Spam-a-considerable measure Can enlist with the expectation of complimentary records consequently Can commandeer transfers, intermediaries Can cloud IP locations Can script mail effortlessly FDIS, August 1, 2003

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Blocking Spam Blacklists Mail, IP addresses from protestations Operations prefers this, holds framework costs down Collateral harm, direct advertisers loathe this Whitelists Buddies, address book, \'individuals I know\' , auto-populate Special promoting courses of action – an issue Filters Keywords, versatile, high-volume marks Weapon of decision yet should stay away from inadvertent blow-back Challenge-Response First time mailer must fill in intelligible structure Rude, issue with receipts, alarms, listservs FDIS, August 1, 2003

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Text Spam Filters Bayesian channels Popular, See "Better Bayesian Filtering" (Jan, 2003). Simple to store word checks and figure probabilities Adaptive, content-based strategy Content is the thing that spammers can\'t shroud Adapt as quick as spammers Algorithms considered Naive Bayes Support Vector Machine Perceptron FDIS, August 1, 2003

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Arms Race Adaptive channels realize what you consider spam Spammers modify (e.g. v\'i\'a\'g\'r\'a, graphical, html tables) Driven to beguiling titles, pictures, seized accounts Check drop boxes to see what gets past More refined customers Picture marks, unicode, vector illustrations Must figure out how to find in \'eye space\' Volume channels Append irregular content to trick signature procedures Chop up mailings in little lumps Hijack open intermediaries, numerous ISPs Scripted programmed free mail enlistments ISPs as of late executed Turing-sort challenge FDIS, August 1, 2003

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Direct Marketing Problems ISPs permit mass mail from customers Significant protestations from this "whitelisted" mail Spam looks verging on indistinguishable to versatile channels Direct Marketers\' Position Would get a kick out of the chance to keep away from spam squares Honest subject and headers Opt-out component Seal of respectability or assent token Legal Approach Laws against tricky publicizing People love thought of \'don\'t spam\' list 90% of spam is untraceable to unique sender Much originates from Korea, China, Pakistan, Colombia, Russia, Japan FDIS, August 1, 2003

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Other Strategies Economic Transfer expense to sender E.g. Initial 100/day free, then $.001 next 1000, $.01 next 100,000 E.g. Make senders post bond which collectors can gather E.g. Make senders play out a register serious errand (scramble?) Much spam originates from clueless casualties of commandeered records Authentication Strip email of namelessness, follow like telephone calls (SS7?) Authenticate with encoded tokens 3 rd party anonymizer Unique computerized stamp for every email Reputation component or trust seal Need to re-engineer email and SMTP FDIS, August 1, 2003

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Is Tide Turning? From Spam-a-ton To Spam-a-geddon? Conclusions? Thoughts? FDIS, August 1, 2003

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