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The podcast discusses enhancing spam filters by following links in spam emails, potentially increasing spammers' costs and decreasing their sales.
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Quick takeaways
- Following links in suspected spams can improve the accuracy of Bayesian spam filters and punish spammers by overloading their servers and increasing their costs.
- Auto retrieving spam filters, by crawling suspected spam URLs and incorporating blacklists, can significantly reduce spam for users.
Deep dives
Improving Bayesian spam filters
Richard Jousie of Death 2 Spam suggests improving the accuracy of Bayesian spam filters by having them follow links in borderline cases. He reports that this approach works well. By following URLs in suspected spams, email clients could filter spam more effectively. This additional feature, called a punish mode, would spider every URL in a suspected spam. It would cause the spammer's servers to experience a heavy load and potentially become unavailable, which would result in higher costs and lower sales for spammers. Such auto retrieving spam filters would rebound against spammers and help the non-gullible majority stop or threaten to stop gullible people from responding to spam.
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highlights
Listen to the best highlights from the podcasts you love and dive into the full episode