your future, yard donkey

I haven’t had time or energy to blodge much. Haven’t been keeping up on other people, either.

My life is a blur of medical bullshit, entertainment news, unix sysadmin tasks, spyware, and spam. Lots and lots of spyware and spam. In lieu of telling you lots of boring details about this I will paste a typical reading item for me lately below.

Content analysis details: (28.0 points, 5.0 required)

pts rule name description
—- ———————- ————————————————–
1.1 EXTRA_MPART_TYPE Header has extraneous Content-type:…type= entry
0.1 FORGED_RCVD_HELO Received: contains a forged HELO
0.0 DK_POLICY_SIGNSOME Domain Keys: policy says domain signs some mails
2.1 TVD_FW_GRAPHIC_ID1 BODY: TVD_FW_GRAPHIC_ID1
0.4 HTML_30_40 BODY: Message is 30% to 40% HTML
1.8 HTML_IMAGE_ONLY_24 BODY: HTML: images with 2000-2400 bytes of words
0.0 HTML_MESSAGE BODY: HTML included in message
3.5 BAYES_99 BODY: Bayesian spam probability is 99 to 100%
[score: 1.0000]
1.5 RAZOR2_CF_RANGE_E8_51_100 Razor2 gives engine 8 confidence level
above 50%
[cf: 100]
0.5 RAZOR2_CHECK Listed in Razor2 (http://razor.sf.net/)
0.5 RAZOR2_CF_RANGE_51_100 Razor2 gives confidence level above 50%
[cf: 100]
4.0 RCVD_IN_BL_SPAMCOP_NET RBL: Received via a relay in bl.spamcop.net
[Blocked – see ]
1.6 URIBL_SBL Contains an URL listed in the SBL blocklist
[URIs: uaikq.hk]
3.0 URIBL_BLACK Contains an URL listed in the URIBL blacklist
[URIs: uaikq.hk]
3.8 URIBL_AB_SURBL Contains an URL listed in the AB SURBL blocklist
[URIs: uaikq.hk]
1.0 PART_CID_STOCK Has a spammy image attachment (by Content-ID)
1.0 PART_CID_STOCK_LESS Has a spammy image attachment (by Content-ID,
more specific)
1.0 STOCK_IMG_HTML Stock spam image part, with distinctive HTML
1.0 STOCK_IMG_HDR_FROM Stock spam image part, with distinctive From line

10 thoughts on “your future, yard donkey

  1. I was wondering where you’d been lately. I’ve missed you! (Not in a gay way, of course.)
    I hope things are okay with you and that the medical bullshit isn’t too awful.

      1. Re: SpamAssassin
        you’re welcome!
        re the user pic: it’s great, isn’t it. All credit to Kubrick, of course… it’s his shot, after all πŸ˜‰

  2. Why not try out a Bayesian filter like Popfile?
    Once you’ve fed a Bayesian 1000 or so spam emails (so, like, a week’s worth), it classifies spam with a better than 99.9% success rate.
    I swear by it. It’s not 100% perfect, but it works way better than… erm… certain spam filters installed by email providers.

    1. This has a Bayesian filter built in to it, and it’s pretty good. Some of the crazy image-only spams outwit those filters pretty badly, so I was glad to find FuzzyOCR at ‘s suggestion.

  3. If you’re using the Barracuda spam filtering system and you’re tired of getting spammed, I recommend *not* clicking the “Report as Spam” button. I’ve been getting a lot less spam ever since I stopped, but it’s not perfect.

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