Blocklist BCP

As many of you may be aware there is a draft document working its way through the Internet Research Task Force (IRTF) discussing best common practices for blocklists. The IRTF is a parallel organization to the IETF and is charged with long term research related to the Internet. The Anti-Spam Working Group was chartered to investigate tools and techniques for dealing with spam.
Recently the ASRG posted a draft of a best practices document aimed at those running blocklists (draft-irtf-asrg-bcp-blacklists-07). This document has been under development for many years. The authors have used this document to share their experiences with running blocklists and their knowledge of what works and what doesn’t.
Best practices documents are never easy to write and consensus can be difficult. But I think that the authors did a good job capturing what the best practices are for blocklists. I do support the document in principle and, in fact, support many of the specific statements and practices outlined there. As with any best practices documents it’s not perfect but overall it reflects the current best practices for blocklists.
Ken Magill’s article about the BCP
Anti-Abuse buzz article about the BCP

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Content based filtering

A spam filter looks at many things when it’s deciding whether or not to deliver a message to the recipients inbox, usually divided into two broad categories – the behaviour of the sender and the content of the message.
When we talk about sender behaviour we’ll often dive headfirst into the technical details of how that’s monitored and tracked – history of mail from the same IP address, SPF records, good reverse DNS, send rates and ramping, polite SMTP level behaviour, DKIM and domain-based reputation and so on. If all of those are OK and the mail still doesn’t get delivered then you might throw up your hands, fall back on “it’s content-based filtering” and not leave it at that.
There’s just as much detail and scope for diagnosis in content-based filtering, though, it’s just a bit more complex, so some delivery folks tend to gloss over it. If you’re sending mail that people want to receive, you’re sure you’re sending the mail technically correctly and you have a decent reputation as a sender then it’s time to look at the content.
You want your mail to look just like wanted mail from reputable, competent senders and to look different to unwanted mail, viruses, phishing emails, botnet spoor and so on. And not just to mechanical spam filters – if a postmaster looks at your email, you want it to look clean, honest and competently put together to them too.
Some of the distinctive content differences between wanted and unwanted email are due to the content as written by the sender, some of them are due to senders of unwanted email trying to hide their identity or their content, but many of them are due to the different quality software used to send each sort of mail. Mail clients used by individuals, and content composition software used by high quality ESPs tends to be well written and complies with both the email and MIME RFCs, and the unwritten best common practices for email composition. The software used by spammers, botnets, viruses and low quality ESPs tends not to do so well.
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GFI/SORBS – a DDoS Intermezzo

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I’ve been stage-managing for a production of The Nutcracker this week, so musical terminology is on my mind. In opera, the intermezzo is a comedic interlude between acts of an opera series.
This comedic interlude is about the “DDoS” – a distributed denial of service attack. What is a denial of service attack?

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GFI/SORBS considered harmful, part 2

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Yesterday I talked about GFI responsiveness to queries and delisting requests about SORBS listings. Today I’m going to look at data accuracy.
The two issues are tightly intertwined – a blacklist that isn’t responsive to reports of false positive listings will end up with a lot of stale or inaccurate data, and a blacklist that has many false positives will likely be overwhelmed with complaints and delisting requests, and won’t be able to respond to them – leading to a spiral of dissatisfaction and inaccurate data feeding off each other.

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