net-dns-dnstwist
Net Dns Dnstwist.
SKILL.mdskills/net-dns-dnstwist/SKILL.md
GitHub") for entry in high_risk[:10]: print(f" {entry['domain']} (score: {entry['risk_score']})") for factor in entry['risk_factors']: print(f" - {factor}")
return {"high": high_risk, "medium": medium_risk, "low": low_risk}
analysis = analyze_results(results, legitimate_ips={"93.184.216.34"})
### Step 3: Continuous Monitoring Pipeline
```python
import time
import hashlib
class TyposquatMonitor:
def __init__(self, domains, known_domains_file="known_typosquats.json"):
self.domains = domains
self.known_file = known_domains_file
self.known_domains = self._load_known()
def _load_known(self):
try:
with open(self.known_file, "r") as f:
return json.load(f)
except FileNotFoundError:
return {}
def _save_known(self):
with open(self.known_file, "w") as f:
json.dump(self.known_domains, f, indent=2)
def scan_all_domains(self):
"""Scan all monitored domains for new typosquats."""
new_findings = []
for domain in self.domains:
results = run_dnstwist_scan(domain)
for entry in results:
domain_key = entry.get("domain", "")
if domain_key not in self.known_domains:
entry["first_seen"] = datetime.now().isoformat()
entry["monitored_domain"] = domain
self.known_domains[domain_key] = entry
new_findings.append(entry)
print(f" [NEW] {domain_key} ({entry.get('fuzzer', '')})")
self._save_known()
print(f"\n[+] New typosquatting domains found: {len(new_findings)}")
return new_findings
def generate_alert(self, findings):
"""Generate alert for new high-risk typosquatting domains."""
analysis = analyze_results(findings)
alerts = []
for entry in analysis["high"]:
alerts.append({
"severity": "HIGH",
"domain": entry["domain"],
"target": entry.get("monitored_domain", ""),
"risk_score": entry["risk_score"],
"risk_factors": entry["risk_factors"],
"dns_a": entry.get("dns_a", []),
"dns_mx": entry.get("dns_mx", []),
"timestamp": datetime.now().isoformat(),
})
return alerts
monitor = TyposquatMonitor(["mycompany.com", "mycompany.org"])
new_findings = monitor.scan_all_domains()
alerts = monitor.generate_alert(new_findings)
Step 4: Export for Blocklist and Takedown
def export_blocklist(analysis, output_file="blocklist.txt"):
"""Export high-risk domains as blocklist for firewall/proxy."""
domains = []
for entry in analysis["high"] + analysis["medium"]:
domain = entry.get("domain", "")
if domain:
domains.append(domain)
with open(output_file, "w") as f:
f.write(f"# Typosquatting blocklist generated {datetime.now().isoformat()}\n")
for d in sorted(set(domains)):
f.write(f"{d}\n")
print(f"[+] Blocklist saved: {len(domains)} domains -> {output_file}")
return domains
def generate_takedown_report(high_risk_domains):
"""Generate takedown request report."""
report = f"""# Domain Takedown Request
Generated: {datetime.now().isoformat()}
## Summary
{len(high_risk_domains)} domains identified as potential typosquatting/phishing.
## Domains Requiring Takedown
"""
for entry in high_risk_domains:
report += f"""
### {entry['domain']}
- **Permutation Type**: {entry.get('fuzzer', 'unknown')}
- **IP Address**: {', '.join(entry.get('dns_a', ['N/A']))}
- **MX Records**: {', '.join(entry.get('dns_mx', ['N/A']))}
- **Risk Score**: {entry.get('risk_score', 0)}
- **Risk Factors**: {'; '.join(entry.get('risk_factors', []))}
- **Web Similarity**: {entry.get('ssdeep_score', 'N/A')}%
"""
with open("takedown_report.md", "w") as f:
f.write(report)
print("[+] Takedown report generated: takedown_report.md")
export_blocklist(analysis)
generate_takedown_report(analysis["high"])
Validation Criteria
- DNSTwist generates domain permutations for target domain
- DNS resolution identifies registered lookalike domains
- Web similarity scoring detects cloned phishing pages
- Risk scoring prioritizes domains by threat level
- Continuous monitoring detects newly registered typosquats
- Blocklist and takedown reports generated correctly