Overview
For clients on our Plesk servers (e.g., Canada20), SpamAssassin is a dynamic filter that relies on Bayesian Learning to adapt to the specific types of "nefarious" mail targeting a business. Success depends on user interaction and technical data.
The "Learning" Mechanism in Plesk
Plesk monitors IMAP folder movements to optimize the filter without over-tightening the sensitivity score.
1. Training the "Spam" Filter
Action: Client moves a message from Inbox → Spam/Junk folder.
Technical Backend: Triggers
sa-learn --spam.
Result: System extracts "tokens" (IPs, phrases, hidden HTML). Future similar emails receive a higher spam score automatically.
2. Training the "Ham" (Legitimate) Filter
Action: Client moves a message from Spam → Inbox.
Technical Backend: Triggers
sa-learn --ham.
Result: "Un-trains" the filter for that sender/format to prevent future false positives.
Plesk Support Workflow for Agents
| Customer Issue | Recommended Response / Action |
|---|---|
| "Spam in Inbox" | 1. Confirm Score is 3.5 - 5.0. 2. Instruct client to Drag-and-Drop to Spam to "Train" the server. |
| "Legit mail in Spam" | 1. Instruct client to Drag to Inbox. 2. Whitelist the sender in Plesk Mail Settings. |
| "Spam is Persistent" | Request Email Headers (details below). |
Critical: The "Email Header" Investigation
If a specific type of spam repeatedly bypasses the filters, the Hosting Team needs to see the "fingerprints" of the email.
The Rule: Instruct the client to forward the spam as an attachment.
Why? A standard forward strips the Email Headers.
The Goal: Headers allow the Hosting Team to inspect the
X-Spam-Status, theReceivedtrail, and theDKIM/SPFresults to see exactly which security layer the spam is slipping through.
Agent Script: "To help our engineers block this at the server level, please forward a few examples as an attachment. This preserves the technical headers we need to identify how these messages are bypassing our current filters."
Important: The IMAP Requirement
For "Training" to work, the client must be using IMAP.
Note: If a client uses POP3, the server cannot "see" what the user does with the email once it is downloaded. No Bayesian training will occur on POP3.
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