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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
2.4 KiB
2.4 KiB
Detailed Hunting Workflow - DNS Tunneling with Zeek
Phase 1: Query Length and Volume Analysis
Step 1.1 - Identify Domains with Long Queries
index=zeek sourcetype=bro_dns
| eval query_len=len(query)
| where query_len > 50
| rex field=query "\.(?<basedomain>[^.]+\.[^.]+)$"
| stats count avg(query_len) as avg_len max(query_len) as max_len dc(query) as unique_queries by id.orig_h basedomain
| where count > 50
| sort -avg_len
Step 1.2 - High Volume DNS to Single Domain
index=zeek sourcetype=bro_dns
| rex field=query "\.(?<basedomain>[^.]+\.[^.]+)$"
| bin _time span=1h
| stats count by id.orig_h basedomain _time
| where count > 100
| sort -count
Phase 2: Entropy Analysis
Step 2.1 - Shannon Entropy Calculation
import math
from collections import Counter
def shannon_entropy(text):
if not text:
return 0.0
counts = Counter(text)
length = len(text)
return -sum((c/length) * math.log2(c/length) for c in counts.values())
# Flag subdomains with entropy > 3.5
Step 2.2 - Splunk Entropy Approximation
index=zeek sourcetype=bro_dns
| rex field=query "^(?<subdomain>[^.]+)"
| where len(subdomain) > 20
| eval has_numbers=if(match(subdomain, "[0-9]"), 1, 0)
| eval has_mixed_case=if(match(subdomain, "[A-Z]") AND match(subdomain, "[a-z]"), 1, 0)
| stats count avg(len(subdomain)) as avg_sub_len sum(has_numbers) as numeric_count by id.orig_h basedomain
| eval numeric_ratio=numeric_count/count
| where avg_sub_len > 25 AND numeric_ratio > 0.3
Phase 3: Record Type Analysis
Step 3.1 - Unusual Record Types
index=zeek sourcetype=bro_dns
| where qtype_name IN ("TXT", "NULL", "CNAME", "MX", "KEY", "SRV")
| rex field=query "\.(?<basedomain>[^.]+\.[^.]+)$"
| stats count dc(query) as unique by id.orig_h basedomain qtype_name
| where count > 50
| sort -count
Phase 4: RITA Automated Analysis
# Full Zeek log import and DNS analysis
rita import /opt/zeek/logs/current dns_hunt
rita show-dns-tunneling dns_hunt
rita show-exploded-dns dns_hunt | sort -k2 -n -r | head -20
Phase 5: Correlation and Response
Step 5.1 - Map DNS Source to Endpoint
Correlate dns.log source IPs with DHCP logs or endpoint inventory to identify affected hosts and processes.
Step 5.2 - Response Actions
- DNS sinkhole the identified tunneling domain
- Block at DNS resolver and firewall
- Isolate source endpoint
- Capture memory and disk forensics
- Assess scope of data exfiltration