detecting-dnp3-protocol-anomalies
by mukul975detecting-dnp3-protocol-anomalies helps analyze DNP3 traffic in SCADA environments to flag unauthorized control commands, protocol violations, restart attempts, and deviations from baseline behavior. Use this detecting-dnp3-protocol-anomalies skill for Security Audit, IDS tuning, and reviewing Zeek logs or packet captures.
This skill scores 68/100, which means it is listable but should be installed by users who specifically need DNP3/SCADA anomaly detection. The repository shows real workflow content, concrete detection indicators, and an executable script, but users will still need some setup judgment because the operational path is only moderately documented.
- Clear DNP3-specific trigger conditions for SCADA, RTU, and substation monitoring
- Concrete detection content: function-code risk table, Zeek log fields, Suricata rules, and Scapy parsing examples
- Includes a Python agent script that parses Zeek/pcap-style inputs and looks for unauthorized hosts and protocol anomalies
- No install command in SKILL.md, so activation/setup steps are not immediately obvious to directory users
- Likely requires OT network access and a baseline of normal DNP3 traffic, which limits plug-and-play usefulness
Overview of detecting-dnp3-protocol-anomalies skill
What this skill is for
The detecting-dnp3-protocol-anomalies skill helps you analyze DNP3 traffic in SCADA environments and flag behavior that looks unsafe, unauthorized, or out of baseline. It is most useful for OT/ICS defenders, security auditors, and detection engineers who need to review DNP3 captures or logs without manually reverse-engineering every packet first.
What it detects well
This detecting-dnp3-protocol-anomalies skill centers on high-value DNP3 indicators such as unauthorized control commands, suspicious function codes, protocol violations, restart attempts, and traffic patterns that deviate from normal master/outstation behavior. It is especially relevant for DNP3 IDS tuning and detecting-dnp3-protocol-anomalies for Security Audit workflows where you need defensible findings, not just a generic “anomaly” label.
Where it fits and where it does not
Use it when DNP3 is in scope and you have packet captures, Zeek logs, or other protocol telemetry from substations or utility networks. Do not use it as a substitute for non-DNP3 protocol analysis, secure authentication design, or broad network anomaly detection; those are different problems and will produce weaker results if forced through this skill.
How to Use detecting-dnp3-protocol-anomalies skill
Install and inspect the skill
Install the detecting-dnp3-protocol-anomalies install path in your skills environment, then read the skill entry point first: SKILL.md. After that, inspect references/api-reference.md for function codes, log fields, and example rules, and scripts/agent.py for the detection logic and expected inputs. If you need to understand the full repository shape, check LICENSE and any supporting files in references/.
Feed it the right input
The detecting-dnp3-protocol-anomalies usage pattern works best when you provide one of these:
- a Zeek
dnp3.log - a pcap containing DNP3 traffic
- a concise description of the monitoring context
- known-good baseline details such as masters, outstations, and maintenance windows
Stronger input looks like: “Analyze this Zeek dnp3.log from a substation, identify abnormal function codes, and separate likely maintenance traffic from suspicious direct-operate activity.” Weak input like “check this network for anomalies” gives the skill too little protocol context.
Use a workflow that mirrors the repository
The repository supports a practical sequence: parse DNP3 traffic, compare it to baseline behavior, inspect high-risk function codes, and then decide whether the event is expected, suspicious, or critical. For best results, tell the model what telemetry you have, what “normal” means in your environment, and whether you want a detection summary, an audit note, or a rule-tuning suggestion. If you are adapting the skill into your own stack, keep the same order: collect, baseline, classify, then report.
Prompting tips that improve output quality
Ask for protocol-specific findings instead of a generic narrative. For example, request “flag off-hours OPERATE or DIRECT_OPERATE events, unknown masters, bursts above baseline, and restart commands” rather than “summarize the file.” If you want detecting-dnp3-protocol-anomalies for Security Audit, say so explicitly and ask for evidence, timestamps, impacted hosts, and confidence so the result is easier to review or hand off.
detecting-dnp3-protocol-anomalies skill FAQ
Is this only for DNP3?
Yes. The skill is tuned for DNP3 traffic in OT/ICS environments, not for Modbus, generic TCP anomaly detection, or unrelated application logs. If your environment mixes protocols, use this skill only on the DNP3 slice.
Do I need packet captures to use it?
Not always. Zeek DNP3 logs are often enough for first-pass triage, while packet captures give you more context when you need to verify function codes or packet sequence details. If you have both, the pcap is better for deep review and the log is better for fast triage.
Is it beginner-friendly?
It is usable by beginners who can provide a capture or log and describe the environment, but the output is most valuable when the user understands basic DNP3 concepts like masters, outstations, and control commands. If you do not know those yet, start with the referenced function-code table and the log-field examples before asking for conclusions.
When should I skip it?
Skip detecting-dnp3-protocol-anomalies if you are trying to design Secure Authentication, investigate a non-DNP3 intrusion, or build a generic SOC alert without OT context. It is also a poor fit if you cannot supply baseline expectations, because anomaly detection depends on knowing what normal looks like.
How to Improve detecting-dnp3-protocol-anomalies skill
Give better baseline context
The biggest quality jump comes from describing normal behavior up front: approved masters, known outstations, expected poll intervals, maintenance windows, and which commands are routine versus rare. Without that, the skill may treat valid operations as suspicious or miss low-and-slow abuse that only stands out against a baseline.
Ask for the outputs you actually need
If you want the result to be useful, specify the format: triage summary, audit findings, detection rule candidates, or incident timeline. For example: “Return a table of suspicious DNP3 events with timestamp, source, destination, function code, reason, and recommended follow-up.” That produces cleaner, more actionable detecting-dnp3-protocol-anomalies usage results than an open-ended explanation.
Watch for common failure modes
The main failure modes are missing context, mixing protocols, and over-trusting one suspicious field without corroboration. A single WRITE or restart command is not always malicious in OT, so provide host allowlists, maintenance context, and any recent change tickets when available. If the first pass is noisy, refine by narrowing the time window or asking the skill to rank findings by severity and confidence.
Iterate with concrete examples
If the first output is too broad, give one or two example events and say why they matter. For example: “This DIRECT_OPERATE at 02:13 UTC is not scheduled maintenance; explain why it is high risk and what evidence would confirm abuse.” That kind of prompt turns detecting-dnp3-protocol-anomalies skill output from a generic alert into a defensible review artifact.