mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
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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