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Introduction:
A detection rule that perfectly maps to MITRE ATT&CK T1003.001 but floods your SOC with false positives is worse than no detection at all. As highlighted by Raylee Hawkins’ recent analysis of 79 Splunk searches across 9 SPL files, the gap between “covers the technique” and “works in production” is where most canned detection content dies—taking analyst trust and incident response velocity with it.
Learning Objectives:
- Identify the three most common operational flaws in SIEM detections (missing time bucketing, weak exclusions, and absent risk scoring)
- Rewrite a flawed LSASS Process Access detection into a production-ready Splunk search using
bin, thresholds, and path-scoped exclusions - Apply analogous hardening patterns to Sigma rules, Wazuh configurations, and Windows Event Log monitoring
You Should Know:
- The “Count Always 1” Trap: Fixing Time Bucketing in Splunk and Elastic
The original LSASS detection used `stats count by _time` without a `bin` command, turning every event into its own bucket. This guarantees a count of 1 per event, rendering any threshold useless. In production, an EDR heartbeat that sneaks past exclusions will fire every few seconds.
Step-by-step fix for Splunk:
index=windows sourcetype=WinEventLog:Security EventCode=4656 ObjectName="LSASS" AccessMask=0x1F3FFF | bin _time span=5m | stats count by _time, Computer, TargetUser | where count > 3
– `bin _time span=5m` groups events into 5-minute buckets.
– `stats count` aggregates per bucket.
– `where count > 3` requires multiple access attempts within the window.
For Elastic (EQL):
sequence by host.name, winlog.event_data.TargetUserName [process where event.code == "4656" and winlog.event_data.ObjectName : "LSASS" and winlog.event_data.AccessMask == "0x1F3FFF"] with maxspan=5m
– The `sequence` with `maxspan` enforces a time-bound correlation.
2. Path-Based Exclusions vs. Substring Nightmares
Excluding `falcon-sensor` as a substring match means an attacker can name their tool `not-falcon-sensor.exe` and bypass the rule. Worse, legitimate processes like `C:\Program Files\SomeApp\falcon-sensor-helper.dll` could be incorrectly excluded or included depending on the regex.
Windows command to verify full path exclusions:
Get-Process | Where-Object { $_.Path -like "falcon-sensor" } | Select-Object Id, ProcessName, Path
Correct Splunk exclusion:
NOT (ImagePath IN ("C:\Program Files\CrowdStrike\", "C:\Windows\System32\"))
Sigma rule equivalent:
detection: selection: Image|contains: 'lsass' exclusion: Image|startswith: - 'C:\Program Files\CrowdStrike\' - 'C:\Windows\System32\' condition: selection and not exclusion
3. Adding Risk Scoring to Survive Day Two
A detection without risk scoring forces analysts to treat every hit as equal. The moment a legitimate admin runs Sysinternals ProcDump, the rule gets disabled. Instead, assign risk based on multiple dimensions: process ancestry, user privilege, and frequency.
Splunk risk scoring example:
index=windows EventCode=4656 ObjectName="LSASS" | eval risk_score = (if(AccessMask="0x1F3FFF", 30, 0)) + (if(ParentProcess="cmd.exe" OR ParentProcess="powershell.exe", 20, 0)) + (if(User!="SYSTEM" AND User!="LOCAL SERVICE", 10, 0)) | where risk_score >= 40 | `send_to_risk_alerting`
Linux equivalent (auditd + custom scoring):
Monitor ptrace or process_vm_readv calls to /proc/pid/mem
auditctl -a always,exit -S process_vm_readv -F pid=1 -k lsass_access
ausearch -k lsass_access --format csv | awk -F, '{score+=30} END {if(score>100) print "ALERT"}'
4. Rebuilding Correlated Detections That Need Full Refactoring
Some detections require moving from single-event logic to multi-stage correlation. For example, LSASS access followed by a scheduled task creation or registry persistence. Raylee flagged that 4 of 79 rules needed complete rebuilds—these were rules trying to cover T1053 (Scheduled Task) without linking process trees.
Step-by-step correlation in Splunk:
index=windows (EventCode=4656 ObjectName="LSASS") OR (EventCode=4698 TaskContent="") | transaction Computer maxspan=10m | where eventcount=2 | table _time, Computer, User, EventCode_4656, EventCode_4698
Wazuh (OSQuery + FIM) correlation:
-- osquery: find LSASS access from non-system processes
SELECT p.pid, p.name, p.cmdline, f.path
FROM processes p
JOIN file f ON p.pid = f.pid
WHERE f.path LIKE '%lsass%' AND p.name NOT IN ('winlogon.exe', 'csrss.exe');
Schedule this query every 30 seconds and alert on any result.
5. From Splunk to Sigma: Portable Detection Engineering
A detection that works only in Splunk is a liability. Convert your hardened logic to Sigma for cross-platform reuse. Sigma rules can be compiled to Splunk, Elastic, QRadar, or Azure Sentinel.
Sigma rule with time bucketing and exclusions:
title: LSASS Access with Path Exclusions and Threshold status: experimental logsource: product: windows service: security detection: selection: EventID: 4656 ObjectName|endswith: '\lsass.exe' AccessMask: '0x1F3FFF' exclusion: Image|startswith: - 'C:\Program Files\CrowdStrike\' - 'C:\Windows\System32\' timeframe: 5m condition: selection | count() by Computer, TargetUser > 3 and not exclusion
Compile with `sigmac -t splunk rule.yml` to generate SPL automatically.
6. Testing Your Detections with Atomic Red Team
Before deploying any detection, validate it against real adversary behavior. Atomic Red Team provides T1003.001 tests that generate LSASS memory access.
Windows command to run Atomic test:
Invoke-AtomicTest T1003.001 -TestNumbers 1
Monitor your SIEM during the test:
- Does your
bin-based rule fire exactly once per 5-minute window? - Does your path exclusion incorrectly block the test tool (e.g.,
C:\Tools\AtomicRedTeam\)? - Does your risk score reach threshold only after repeated attempts?
Linux test for /proc/kcore access (analogous to LSASS):
sudo dd if=/proc/kcore of=/dev/null bs=1024 count=1 sudo auditctl -w /proc/kcore -p r -k kernel_mem_access ausearch -k kernel_mem_access -ts recent
What Undercode Say:
- Canned detections are starting points, not endpoints. A MITRE tag without operational hardening—time bucketing, path scoping, risk scoring—is a false sense of security. The most dangerous detection is the one that fires constantly until an analyst disables it, leaving a blind spot exactly where attackers operate.
- Shift left from “technique coverage” to “survival engineering.” The real craft of detection engineering is writing rules that survive week two in a SOC. That means anticipating noise, building in resilience through thresholds, and making exclusions so specific that attackers can’t slide through with a renamed binary.
Prediction:
Within 18 months, SIEM vendors will begin shipping “production-hardened” detection tiers that automatically inject time bucketing, dynamic risk scoring, and path-based exclusions into community rules. However, this will only raise the baseline—attackers will pivot to abusing trust relationships and living-off-the-land binaries (LOLBins) that bypass path-based logic entirely. The next wave of detection engineering will focus on behavioral baselining and anomaly scoring at the endpoint, moving away from static MITRE mappings toward continuous, context-aware risk quantification. SOCs that still rely on “copy-paste detections” in 2026 will find their alert queues empty not because they are secure, but because they’ve been blind-trusted into irrelevance.
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