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2025-02-15
When I first started working with SIEM Query Languages, it felt like diving into the unknown! To help others navigate this complex landscape, I’ve put together a high-level survival guide with examples, resources, and tips. My goal is to inspire others to find solutions and tackle the challenge of learning new systems like these. Check it out!
URL: huntbook.predefender.com
Practice-Verified Codes and Commands
Here are some practical examples of SIEM query commands and scripts to help you get started:
Splunk Query Example
[splunk]
index=main sourcetype=access_combined status=200 action=success
| stats count by src_ip
| sort – count
[/splunk]
This query filters successful HTTP 200 responses, groups them by source IP, and sorts them in descending order.
Elasticsearch Query Example
{
"query": {
"bool": {
"must": [
{ "match": { "event.type": "login" }},
{ "range": { "@timestamp": { "gte": "now-1d/d" }}}
]
}
}
}
This query searches for login events within the last day.
Sigma Rule Example
title: Suspicious Process Creation description: Detects suspicious process creation patterns author: Undercode logsource: product: windows service: sysmon detection: selection: EventID: 1 Image|endswith: - 'powershell.exe' - 'cmd.exe' condition: selection
This Sigma rule detects suspicious process creation events in Windows environments.
Linux Command for Log Analysis
grep "Failed password" /var/log/auth.log | awk '{print $9}' | sort | uniq -c | sort -nr
This command extracts failed login attempts from the auth log, groups them by username, and sorts them by frequency.
What Undercode Say
SIEM query languages are essential for modern cybersecurity operations, enabling professionals to detect, analyze, and respond to threats effectively. Whether you’re working with Splunk, Elasticsearch, or Sigma rules, mastering these tools is crucial for threat hunting and incident response.
For Splunk, start by familiarizing yourself with the Search Processing Language (SPL). Use commands like stats, sort, and `eval` to manipulate and analyze data. For Elasticsearch, leverage the Query DSL to create complex queries that filter and aggregate logs. Sigma rules provide a standardized way to describe detection logic, making it easier to share and implement across different SIEM platforms.
In Linux, log analysis is a critical skill. Commands like grep, awk, and `sort` are invaluable for parsing and analyzing log files. For Windows, Sysmon is a powerful tool for monitoring process creation, network connections, and file changes. Use Event IDs to filter specific activities and create custom rules to detect anomalies.
To further enhance your skills, explore resources like the Predefender Threat Hunt Book and practice with real-world datasets. Combine these tools with scripting languages like Python to automate repetitive tasks and build custom detection pipelines.
Remember, cybersecurity is a constantly evolving field. Stay updated with the latest threats, tools, and techniques by following reputable sources and participating in communities. Continuous learning and hands-on practice are the keys to success in this domain.
Additional Resources:
By combining theoretical knowledge with practical experience, you can become proficient in SIEM query languages and significantly improve your organization’s security posture.
References:
Hackers Feeds, Undercode AI
