Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add killvxk/cybersecurity-skills-zh --skill analyzing-network-traffic-for-incidentsgit clone --depth 1 https://github.com/killvxk/cybersecurity-skills-zhWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-network-traffic-for-incidents)<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-network-traffic-for-incidents"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-network-traffic-for-incidents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/killvxk/cybersecurity-skills-zh/analyzing-network-traffic-for-incidents"><img src="https://agentmods.dev/badge/skills/killvxk/cybersecurity-skills-zh/analyzing-network-traffic-for-incidents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.03074 |
| Opus 5 | $0.00057 | $0.01537 |
| Sonnet 5 | $0.00023 | $0.00615 |
| Haiku 4.5 | $0.00011 | $0.00307 |
Grade A, and why
analyzing-network-traffic-for-incidents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
针对安全事件的网络流量分析
适用场景
- SIEM 告警显示异常网络流量模式,需要深入调查
- 怀疑存在 C2 信标(beaconing),需通过数据包级别分析加以确认
- 需要从网络证据中量化数据外泄的规模或目标地址
- 需要通过网络连接追踪系统间的横向移动
- IDS/IPS 告警需要数据包级别验证以确认或排除
不适用于基于主机的取证分析(进程执行、文件系统制品);此类场景请使用终端取证工具。
前置条件
- 全量数据包捕获(PCAP)基础设施或按需捕获能力(网络分路器、SPAN 端口)
- 分析工作站上已安装 Wireshark,并掌握相应显示过滤器知识
- 已部署 Zeek(原 Bro)用于生成网络元数据(conn.log、dns.log、http.log、ssl.log)
- 从网络设备收集 NetFlow/IPFIX,用于流量分析
- 网络架构图,展示 VLAN 布局、防火墙位置和监控点
- 威胁情报(threat intelligence)源,用于关联观察到的网络失陷指标(IOC)
工作流程
步骤 1:捕获或获取网络流量
获取调查所需的相关流量数据:
实时捕获(事件仍在进行时):
# 在特定接口上按主机过滤进行捕获
tcpdump -i eth0 -w capture.pcap host 10.1.5.42
# 捕获发往特定外部 IP 的 C2 流量
tcpdump -i eth0 -w c2_traffic.pcap host 185.220.101.42
# 带滚动轮转的捕获(每个文件 1GB,保留 10 个)
tcpdump -i eth0 -w capture_%Y%m%d%H%M.pcap -C 1000 -W 10
从现有基础设施获取:
- 从全量数据包捕获设备导出 PCAP(Arkime/Moloch、ExtraHop、Corelight)
- 从 Zeek 集群拉取调查时间段内的 Zeek 日志
- 从网络设备导出 NetFlow 数据,用于高层流量分析
步骤 2:识别 C2 通信
检测命令与控制流量模式:
信标检测(Zeek conn.log):
# 提取具有规律间隔的对外连接
cat conn.log | zeek-cut ts id.orig_h id.resp_h id.resp_p duration orig_bytes resp_bytes \
| awk '$4 ~ /^185\.220/' | sort -t. -k1,1n -k2,2n
Wireshark 信标分析:
# 过滤发往可疑 C2 IP 的流量
ip.addr == 185.220.101.42
# 过滤非标准端口上的 HTTPS 流量
tcp.port != 443 && ssl
# 过滤可疑域名的 DNS 查询
dns.qry.name contains "evil" or dns.qry.name matches "^[a-z0-9]{32}\."
# 过滤 HTTP POST(常见 C2 上线方式)
http.request.method == "POST" && ip.dst == 185.220.101.42
信标特征识别要点:
- 连接之间存在规律性时间间隔(例如每 60 秒,抖动(jitter)10-15%)
- 请求和响应中数据包大小保持一致
- HTTPS 连接发往与合法 CDN 或服务无关的外部 IP
- DNS 查询子域熵值高(DNS 隧道(DNS tunneling)指标)
步骤 3:分析横向移动流量
追踪攻击者在内部系统间的移动路径:
横向移动检测关键协议:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SMB (TCP 445): PsExec、文件共享访问、勒索软件传播
RDP (TCP 3389): 远程桌面会话
WinRM (TCP 5985): PowerShell 远程访问
WMI (TCP 135): 远程命令执行
SSH (TCP 22): Linux 横向移动
DCE/RPC (TCP 135): 基于 DCOM 的横向移动
横向移动的 Wireshark 过滤器:
# SMB 横向移动
smb2 && ip.src == 10.1.5.42 && ip.dst != 10.1.5.42
# 来自失陷主机的 RDP 连接
tcp.dstport == 3389 && ip.src == 10.1.5.42
# Kerberos 票据请求(潜在的票据传递(Pass-the-Ticket))
kerberos.msg_type == 12 && ip.src == 10.1.5.42
# NTLM 认证(潜在的哈希传递(Pass-the-Hash))
ntlmssp.auth.username && ip.src == 10.1.5.42
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 248 lines · 114 tokens per session scan A 26d072784a16
analyzing-network-traffic-for-incidents is a skill published in the GitHub repository killvxk/cybersecurity-skills-zh (44 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 114 tokens to every session and 3,074 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…
analyzing-network-traffic-for-incidents
Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic…