Analyzing Indicators of Compromise

When to Use

Use this skill when:

• A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
• Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
• An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

Prerequisites

• VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
• AbuseIPDB API key for IP reputation checks
• MISP instance or TIP for cross-referencing against known campaigns
• Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

Workflow

Step 1: Normalize and Classify IOC Types

Before enriching, classify each IOC:

• IPv4/IPv6 address: Check if RFC 1918 private (skip external enrichment), validate format
• Domain/FQDN: Defang for safe handling (evil[.]com), extract registered domain via tldextract
• URL: Extract domain + path separately; check for redirectors
• File hash: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
• Email address: Split into domain (check MX/DMARC) and local part for pattern analysis

Defang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.

Step 2: Multi-Source Enrichment

VirusTotal (file hash, URL, IP, domain):

import vt

client = vt.Client("YOUR_VT_API_KEY")

# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")

# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()

AbuseIPDB (IP addresses):

import requests

response = requests.get(
    "https://api.abuseipdb.com/api/v2/check",
    headers={"Key": "YOUR_KEY", "Accept": "application/json"},
    params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")

MalwareBazaar (file hashes):

response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
    print(result["data"][0]["tags"], result["data"][0]["signature"])

Step 3: Contextualize with Campaign Attribution

Query MISP for existing events matching the IOC:

from pymisp import PyMISP

misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
    print(event["Event"]["info"], event["Event"]["threat_level_id"])

Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).

Step 4: Assign Confidence Score and Disposition

Apply a tiered decision framework:

• Block (High Confidence ≥ 70%): ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
• Monitor/Alert (Medium 40–69%): 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
• Whitelist/Investigate (Low <40%): ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
• False Positive: Legitimate business service incorrectly flagged; document and exclude from future alerts

Step 5: Document and Distribute

Record findings in TIP/MISP with:

• All enrichment data collected (timestamps, source, score)
• Disposition decision and rationale
• Blocking actions taken (firewall, proxy, DNS sinkhole)
• Related incident ticket number

Export to STIX indicator object with confidence field set appropriately.

Key Concepts

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