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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
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
1.9 KiB
1.9 KiB
API Reference: Hunting for Unusual Network Connections
Connection Analysis Indicators
| Indicator | Threshold | Severity |
|---|---|---|
| Known bad port (4444, 31337) | Any connection | CRITICAL |
| Non-standard port | Not in common set | MEDIUM |
| Rare destination (< 3 conns) | Unique in environment | HIGH |
| Long connection (> 1hr) | Duration > 3600s | HIGH |
| Periodic beaconing (CV < 0.3) | Low interval variance | CRITICAL |
Splunk SPL - Rare Destinations
index=firewall action=allowed
| stats dc(src_ip) as src_count count by dest_ip dest_port
| where src_count == 1 AND count < 5
| sort -count
| table dest_ip dest_port count src_count
KQL - Non-Standard Ports
DeviceNetworkEvents
| where RemotePort !in (80, 443, 53, 22, 25, 8080)
| summarize ConnectionCount=count(), dcount(DeviceId) by RemoteIP, RemotePort
| where ConnectionCount < 5
| sort by ConnectionCount asc
Zeek conn.log Analysis
from zat.log_to_dataframe import LogToDataFrame
df = LogToDataFrame().create_dataframe("conn.log")
# Filter rare external destinations
external = df[~df["id.resp_h"].str.startswith(("10.", "172.16.", "192.168."))]
rare = external.groupby("id.resp_h").size().reset_index(name="count")
rare = rare[rare["count"] < 3]
Beaconing Detection
import numpy as np
intervals = np.diff(sorted_timestamps)
cv = np.std(intervals) / np.mean(intervals)
# CV < 0.3 = high periodicity (likely beacon)
Sysmon Event ID 3 (Network Connection)
<EventData>
<Data Name="Image">C:\Windows\System32\svchost.exe</Data>
<Data Name="DestinationIp">203.0.113.50</Data>
<Data Name="DestinationPort">4444</Data>
</EventData>