Security Research Use Cases

SPYDER provides valuable capabilities for cybersecurity professionals, researchers, and organizations to map internet infrastructure and identify security-relevant relationships.

Threat Intelligence Applications

Infrastructure Mapping

Adversary Infrastructure Discovery:

SPYDER can help map threat actor infrastructure by analyzing domain relationships:

bash

# Map suspected threat actor domains
echo "suspicious-domain.com" > threat-domains.txt
./bin/spyder -domains=threat-domains.txt -concurrency=64

Key Relationships to Analyze:

DNS Resolution: Shared IP addresses indicate common hosting
Certificate Usage: Shared certificates suggest common operators
Nameserver Patterns: Common DNS providers may indicate relationships
Link Analysis: Cross-references between malicious sites

Example Analysis:

json

{
  "edges": [
    {
      "type": "RESOLVES_TO",
      "source": "malware-c2.example",
      "target": "192.168.1.100",
      "observed_at": "2024-01-01T12:00:00Z"
    },
    {
      "type": "RESOLVES_TO", 
      "source": "phishing-site.example",
      "target": "192.168.1.100",
      "observed_at": "2024-01-01T12:05:00Z"
    }
  ]
}

Analysis: Both domains resolve to the same IP, suggesting common hosting or operator.

Certificate Transparency Analysis

Shared Certificate Detection:

bash

# Analyze certificate relationships
./bin/spyder -domains=target-domains.txt -exclude_tlds=gov,mil

Certificate Intelligence Use Cases:

Wildcard Certificate Abuse: Detect unauthorized use of legitimate wildcards
Certificate Authority Patterns: Identify preferred CAs of threat actors
Certificate Timing: Analyze certificate issuance patterns
Subject Alternative Names: Discover additional domains on certificates

Example Certificate Analysis:

json

{
  "nodes_cert": [
    {
      "spki_sha256": "B7+tPUdz9OYBgGpZFY9V1MyXOsL88K8AJ2m2Jv8YsPM=",
      "subject_cn": "*.suspicious-domain.com",
      "issuer_cn": "Let's Encrypt Authority X3",
      "not_before": "2024-01-01T00:00:00Z",
      "not_after": "2024-04-01T00:00:00Z"
    }
  ]
}

Typosquatting Detection

Domain Similarity Analysis:

SPYDER can help identify typosquatting by analyzing domains that:

Resolve to similar IP ranges
Use similar certificate patterns
Share nameserver infrastructure
Cross-reference each other

Implementation:

bash

# Generate typosquat candidates
python3 generate-typosquats.py target-company.com > typosquats.txt

# Analyze with SPYDER
./bin/spyder -domains=typosquats.txt -ua="SecurityResearch/1.0"

Attack Surface Analysis

External Asset Discovery

Organization Asset Mapping:

bash

# Map organization's external infrastructure
echo -e "company.com\nsubsidiary.com\nacquisition.com" > org-domains.txt
./bin/spyder -domains=org-domains.txt -concurrency=128

Asset Types Discovered:

Subdomains: Through certificate SAN analysis
CDN Usage: Via CNAME and link analysis
Third-party Services: Through external links
Email Infrastructure: Via MX record analysis
DNS Providers: Through NS record analysis

Shadow IT Detection

Unauthorized Service Discovery:

SPYDER can identify shadow IT by analyzing:

Unexpected external links from corporate domains
Certificate issuance to unauthorized subdomains
DNS patterns indicating cloud service usage

Analysis Queries:

bash

# Look for cloud service indicators
grep -E "(amazonaws|azure|googleapis)" spyder-output.json

# Find unexpected certificate authorities
jq '.nodes_cert[] | select(.issuer_cn | contains("DigiCert"))' spyder-output.json

Incident Response

Compromise Assessment

Infrastructure Relationship Analysis:

During incident response, SPYDER helps identify:

Lateral Infrastructure: Domains sharing resources with compromised assets
Command & Control: External domains linked from compromised sites
Data Exfiltration: Unusual external connections

Rapid Assessment:

bash

# Quick assessment of compromised domain
echo "compromised-domain.com" > incident-domains.txt
./bin/spyder -domains=incident-domains.txt -concurrency=32 -batch_flush_sec=1

Attribution Analysis

Infrastructure Correlation:

json

{
  "analysis": {
    "shared_ip_clusters": [
      {
        "ip": "185.220.101.x",
        "domains": ["site1.com", "site2.com", "site3.com"],
        "pattern": "bulletproof_hosting"
      }
    ],
    "certificate_patterns": [
      {
        "issuer": "Let's Encrypt",
        "timing": "batch_issued_2024_01_01",
        "domains": ["domain1.com", "domain2.com"]
      }
    ]
  }
}

Vulnerability Research

Attack Path Discovery

Infrastructure Dependency Mapping:

SPYDER helps identify attack paths by mapping:

DNS Dependencies: Critical nameserver relationships
Certificate Dependencies: Shared PKI infrastructure
Content Dependencies: External resource loading

Example Attack Path Analysis:

bash

# Map critical infrastructure dependencies
./bin/spyder -domains=critical-services.txt -ua="VulnResearch/1.0"

# Analyze output for single points of failure
jq '.edges[] | select(.type=="USES_NS")' output.json | \
  jq -r '.target' | sort | uniq -c | sort -nr

Supply Chain Analysis

Third-party Dependency Mapping:

bash

# Analyze third-party dependencies
./bin/spyder -domains=target-domains.txt | \
  jq '.edges[] | select(.type=="LINKS_TO")' | \
  jq -r '.target' | sort | uniq > third-party-deps.txt

Supply Chain Risk Indicators:

High-risk hosting providers
Deprecated or vulnerable services
Concentration of dependencies
Geographic risk factors

Threat Hunting

Behavioral Analysis

Pattern-based Hunting:

SPYDER data enables hunting for suspicious patterns:

python

# Example: Hunt for fast-flux patterns
import json

def analyze_dns_patterns(spyder_data):
    ip_to_domains = {}
    for edge in spyder_data['edges']:
        if edge['type'] == 'RESOLVES_TO':
            ip = edge['target']
            domain = edge['source']
            if ip not in ip_to_domains:
                ip_to_domains[ip] = []
            ip_to_domains[ip].append(domain)
    
    # Look for IPs with many domains (potential fast-flux)
    suspicious_ips = {ip: domains for ip, domains in ip_to_domains.items() 
                     if len(domains) > 10}
    
    return suspicious_ips

IOC Expansion

Infrastructure Pivoting:

bash

# Start with known IOC and expand
echo "known-bad-domain.com" > seed-iocs.txt
./bin/spyder -domains=seed-iocs.txt

# Extract related infrastructure
jq '.edges[] | select(.source=="known-bad-domain.com")' output.json | \
  jq -r '.target' > expanded-iocs.txt

Compliance and Risk Assessment

Regulatory Compliance

Infrastructure Auditing:

SPYDER helps with compliance by:

Mapping data flow paths through DNS analysis
Identifying third-party processors via link analysis
Documenting certificate management practices
Tracking geographic distribution of infrastructure

GDPR Compliance Example:

bash

# Map EU vs non-EU infrastructure
./bin/spyder -domains=eu-domains.txt | \
  python3 analyze-geographic-distribution.py

Risk Quantification

Infrastructure Risk Scoring:

python

def calculate_risk_score(spyder_data):
    risk_factors = {
        'shared_hosting': 0.3,
        'weak_certificates': 0.4, 
        'suspicious_links': 0.5,
        'geographic_risk': 0.2
    }
    
    # Analyze SPYDER data for risk factors
    # Return risk score

Research and Analytics

Internet Measurement Studies

Large-scale Infrastructure Studies:

bash

# Analyze top 1M domains (requires significant resources)
./bin/spyder -domains=top1m-domains.txt \
  -concurrency=1024 \
  -batch_max_edges=50000 \
  -ingest=https://research-ingest.university.edu/v1/batch

Research Applications:

Certificate authority ecosystem analysis
CDN adoption and geographic distribution
DNS resolver diversity studies
TLS configuration trends

Competitive Intelligence

Market Analysis:

SPYDER can support competitive intelligence by analyzing:

Technology stack choices (inferred from infrastructure)
Geographic expansion patterns
Third-party service adoption
Security posture indicators

Ethical and Legal Considerations

Responsible Use

Best Practices:

Respect robots.txt directives (SPYDER does this automatically)
Use appropriate rate limiting to avoid impact
Include contact information in User-Agent strings
Document legitimate research purposes

Example Responsible Configuration:

bash

./bin/spyder \
  -domains=research-domains.txt \
  -ua="SecurityResearch/1.0 (+https://university.edu/security-research)" \
  -concurrency=64 \
  -exclude_tlds=gov,mil,int,edu

Data Handling

Privacy Protection:

Limit data collection to necessary infrastructure metadata
Implement data retention policies
Secure storage of collected intelligence
Anonymize data when sharing research results

Legal Compliance

Jurisdiction Considerations:

Comply with local computer crime laws
Respect intellectual property rights
Consider export control regulations
Document legitimate research purposes

Case Studies

Case Study 1: APT Infrastructure Mapping

Scenario: Cybersecurity team needs to map Advanced Persistent Threat (APT) infrastructure.

Approach:

Seed SPYDER with known APT domains from threat intelligence
Analyze DNS, certificate, and link relationships
Identify infrastructure clusters and patterns
Generate additional IOCs for blocking/monitoring

Results:

Discovered 15 additional domains sharing infrastructure
Identified 3 distinct IP ranges used by the threat actor
Found common certificate issuer patterns
Enabled proactive blocking of future campaigns

Case Study 2: Supply Chain Risk Assessment

Scenario: Enterprise needs to assess third-party dependencies for critical web applications.

Approach:

Map all external dependencies using SPYDER link analysis
Analyze certificate and hosting patterns of dependencies
Assess geographic and regulatory risk factors
Prioritize high-risk dependencies for review

Results:

Identified 200+ third-party dependencies
Found 12 dependencies using high-risk hosting
Discovered 3 critical dependencies with weak security posture
Enabled targeted security reviews and supplier assessments

These security use cases demonstrate SPYDER's value in supporting various cybersecurity objectives while maintaining ethical and responsible research practices.

Security Research Use Cases ​

Threat Intelligence Applications ​

Infrastructure Mapping ​

Certificate Transparency Analysis ​

Typosquatting Detection ​

Attack Surface Analysis ​

External Asset Discovery ​

Shadow IT Detection ​

Incident Response ​

Compromise Assessment ​

Attribution Analysis ​

Vulnerability Research ​

Attack Path Discovery ​

Supply Chain Analysis ​

Threat Hunting ​

Behavioral Analysis ​

IOC Expansion ​

Compliance and Risk Assessment ​

Regulatory Compliance ​

Risk Quantification ​

Research and Analytics ​

Internet Measurement Studies ​

Competitive Intelligence ​

Ethical and Legal Considerations ​

Responsible Use ​

Data Handling ​

Legal Compliance ​

Case Studies ​

Case Study 1: APT Infrastructure Mapping ​

Case Study 2: Supply Chain Risk Assessment ​

Security Research Use Cases

Threat Intelligence Applications

Infrastructure Mapping

Certificate Transparency Analysis

Typosquatting Detection

Attack Surface Analysis

External Asset Discovery

Shadow IT Detection

Incident Response

Compromise Assessment

Attribution Analysis

Vulnerability Research

Attack Path Discovery

Supply Chain Analysis

Threat Hunting

Behavioral Analysis

IOC Expansion

Compliance and Risk Assessment

Regulatory Compliance

Risk Quantification

Research and Analytics

Internet Measurement Studies

Competitive Intelligence

Ethical and Legal Considerations

Responsible Use

Data Handling

Legal Compliance

Case Studies

Case Study 1: APT Infrastructure Mapping

Case Study 2: Supply Chain Risk Assessment