detecting-kerberoasting-attacks

name	detecting-kerberoasting-attacks
description	Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests targeting service accounts with SPNs for offline password cracking. Use when detecting kerberoasting attacks by monitoring for anomalous kerberos tgs requests.
domain	cybersecurity
tags	["threat-hunting","mitre-attack","kerberoasting","credential-access","kerberos","t1558","proactive-detection"]
subdomain	threat-hunting
version	1.0
author	oyi77
license	Apache-2.0
d3fend_techniques	["Application Protocol Command Analysis","Network Isolation","Network Traffic Analysis","Client-server Payload Profiling","Network Traffic Community Deviation"]
nist_csf	["DE.CM-01","DE.AE-02","DE.AE-07","ID.RA-05"]

Detecting Kerberoasting Attacks

Overview

Cybersecurity skill for detecting kerberoasting attacks. Follows industry best practices and security standards.

When to Use

Trigger phrases:

• "detecting kerberoasting attacks"

• "When proactively hunting for indicators of detecting kerberoasting attacks in th"

• "After threat intelligence indicates active campaigns using these techniques"

• "During incident response to scope compromise related to these techniques"

• When proactively hunting for indicators of detecting kerberoasting attacks in the environment

• After threat intelligence indicates active campaigns using these techniques

• During incident response to scope compromise related to these techniques

• When EDR or SIEM alerts trigger on related indicators

• During periodic security assessments and purple team exercises

When NOT to Use

• When you lack proper authorization for testing
• For production systems without change management
• When the task requires legal or compliance expertise beyond technical scope

Prerequisites

• EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
• SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
• Sysmon deployed with comprehensive configuration
• Windows Security Event Log forwarding enabled
• Threat intelligence feeds for IOC correlation

Workflow

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text)  k, v  IOC_PATTERNS.items()}