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https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
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mukul975
4d6d585285
Skills added: - implementing-privileged-access-workstation (IAM, PAW hardening) - detecting-suspicious-oauth-application-consent (cloud security, Graph API) - performing-hardware-security-module-integration (cryptography, PKCS#11) - analyzing-android-malware-with-apktool (malware analysis, androguard) - hunting-for-unusual-service-installations (threat hunting, T1543.003) - detecting-shadow-it-cloud-usage (cloud security, proxy/DNS log analysis) - performing-active-directory-forest-trust-attack (red team, impacket) - implementing-deception-based-detection-with-canarytoken (deception, Canary API) - analyzing-office365-audit-logs-for-compromise (cloud security, BEC detection) - hunting-for-startup-folder-persistence (threat hunting, T1547.001) Each skill includes SKILL.md, LICENSE, scripts/agent.py, references/api-reference.md
62 lines
2.3 KiB
Markdown
62 lines
2.3 KiB
Markdown
# API Reference — Detecting Shadow IT Cloud Usage
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## Libraries Used
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- **pandas**: DataFrame aggregation for traffic analysis — groupby, agg, nunique
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- **tldextract**: Accurate registered domain extraction from URLs/hostnames
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- **csv**: CSV log parsing with DictReader
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- **re**: Regex parsing for Squid proxy and BIND DNS query log formats
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## CLI Interface
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```
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python agent.py access.log --type proxy parse
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python agent.py access.log --type proxy analyze
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python agent.py dns-queries.log --type dns full
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python agent.py traffic.csv --type csv --approved approved.txt full
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```
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## Core Functions
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### `parse_proxy_log(filepath)` — Parse Squid/common proxy access logs
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Regex pattern matches Squid format: `timestamp duration client_ip status bytes method url`.
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Falls back to Apache Common Log Format parsing.
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### `parse_dns_log(filepath)` — Parse BIND/named DNS query logs
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Extracts query name and type from `query: DOMAIN IN TYPE` patterns.
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Strips trailing dots from FQDNs.
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### `parse_csv_log(filepath)` — Parse generic CSV traffic logs
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Expects columns: timestamp, src_ip, dst_domain, bytes_out, bytes_in.
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### `analyze_traffic(records)` — Aggregate and classify traffic
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Uses pandas groupby on domain: total_bytes (sum), request_count (count),
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unique_users (nunique). Falls back to collections.defaultdict if pandas unavailable.
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### `classify_domain(domain)` — Categorize against SaaS database
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Categories: storage, email, dev_tools, ai_ml, messaging, file_sharing, vpn_proxy.
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### `full_audit(log_path, log_type, approved_list)` — Complete shadow IT audit
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## Risk Scoring
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| Factor | Points |
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|--------|--------|
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| Unapproved domain | +30 |
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| Storage/file-sharing/VPN category | +25 |
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| Email category | +15 |
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| Data volume (per 10 MB) | +1 (max 20) |
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| Unique users (per user) | +3 (max 15) |
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## SaaS Category Database
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| Category | Example Domains |
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|----------|----------------|
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| storage | dropbox.com, box.com, mega.nz, wetransfer.com |
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| email | protonmail.com, tutanota.com, guerrillamail.com |
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| dev_tools | github.com, gitlab.com, replit.com |
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| ai_ml | chat.openai.com, claude.ai, huggingface.co |
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| messaging | telegram.org, discord.com, signal.org |
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| file_sharing | pastebin.com, file.io, gofile.io |
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| vpn_proxy | nordvpn.com, expressvpn.com, protonvpn.com |
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## Dependencies
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- `pandas` >= 1.5.0
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- `tldextract` >= 3.4.0 (optional, improves domain extraction accuracy)
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