<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Detection on ShieldedBytes</title>
    <link>https://linuxeries.org/tags/detection/</link>
    <description>Recent content in Detection on ShieldedBytes</description>
    <generator>Hugo</generator>
    <language>en</language>
    <lastBuildDate>Sun, 08 Nov 2026 11:25:34 +0200</lastBuildDate>
    <atom:link href="https://linuxeries.org/tags/detection/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>Tuning SIEM Correlation Rules to Cut False Positives on Azure AD Sign‑Ins</title>
      <link>https://linuxeries.org/post/2026-11-08-tuning-siem-correlation-rules-to-cut-false-po/</link>
      <pubDate>Sun, 08 Nov 2026 11:25:34 +0200</pubDate>
      <guid>https://linuxeries.org/post/2026-11-08-tuning-siem-correlation-rules-to-cut-false-po/</guid>
      <description>&lt;p&gt;Azure AD sign‑in logs are a goldmine for detecting compromise, but they also flood SIEMs with benign events that look suspicious at first glance. A well‑tuned correlation rule set can reduce noise while preserving detection quality. Below is a step‑by‑step guide that balances precision, maintainability, and operational overhead.&lt;/p&gt;&#xA;&lt;h3 id=&#34;1-start-with-a-baseline-understand-the-noise-profile&#34;&gt;1. Start with a Baseline: Understand the Noise Profile&lt;/h3&gt;&#xA;&lt;p&gt;Before tweaking any rule, run a 30‑day baseline to capture normal activity. Export the raw sign‑in logs to a CSV or a SIEM table and run a quick aggregation:&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
