Extract DPAPI-protected secrets such as credentials and browser data offline and online.
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andycungkrinx91/konoha122026年10月26日 更新
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detecting-beaconing-patterns-with-zeek
Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.
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查看安装方法包含的文件（3）
- SKILL.md2.6 KB
- references/api-reference.md1.8 KB
- scripts/agent.py6.3 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Detecting Beaconing Patterns with Zeek
When to Use
- When investigating security incidents that require detecting beaconing patterns with zeek
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques
Prerequisites
- Familiarity with security operations concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Instructions
Load Zeek conn.log data using ZAT (Zeek Analysis Tools), group connections by source/destination pairs, and compute timing statistics to identify beaconing.
from zat.log_to_dataframe import LogToDataFrame
import numpy as np
log_to_df = LogToDataFrame()
conn_df = log_to_df.create_dataframe('/path/to/conn.log')
# Group by src/dst pair and calculate inter-arrival time
for (src, dst), group in conn_df.groupby(['id.orig_h', 'id.resp_h']):
times = group['ts'].sort_values()
intervals = times.diff().dt.total_seconds().dropna()
if len(intervals) > 10:
std_dev = np.std(intervals)
mean_interval = np.mean(intervals)
# Low std_dev relative to mean = likely beaconing
Key analysis steps:
- Parse Zeek conn.log into DataFrame with ZAT LogToDataFrame
- Group connections by source IP and destination IP pairs
- Calculate inter-arrival time intervals between consecutive connections
- Compute standard deviation and coefficient of variation
- Flag pairs with low coefficient of variation as potential beacons
Examples
from zat.log_to_dataframe import LogToDataFrame
log_to_df = LogToDataFrame()
df = log_to_df.create_dataframe('conn.log')
print(df[['id.orig_h', 'id.resp_h', 'ts', 'duration']].head())
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中文简介正在准备中。当前显示原文说明。
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中文简介正在准备中。当前显示原文说明。
andycungkrinx91/konoha122026年10月21日 更新
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中文简介正在准备中。当前显示原文说明。
andycungkrinx91/konoha122026年10月26日 更新
Prepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
中文简介正在准备中。当前显示原文说明。
andycungkrinx91/konoha122026年10月21日 更新