Detecting Attacks On Scada Systems

---
name: Tespit etme-attacks-on-scada-systems
description: bu skill covers Tespit etme cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command
  injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion Tespit systems, industrial protocol
  anomaly Tespit, and process data analytics to identify attacks that traditional IT security tools miss.
tags:
- ics
- ot-security
- iec62443
- industrial-control
- ot-ics-security
- intrusion-Tespit
- fetih
- scada
- cybersecurity
- threat-Tespit
- siber-güvenlik
triggers:
- alert
- api
- attacks
- Tespit etme
- exploit
- forensic
- http
- incident
- log
- malware
- network
- scada
category: ot-ics-security
source_subdomain: ot-ics-security
nist_csf:
- PR.IR-01
- DE.CM-01
- ID.AM-05
- GV.OC-02
adapted_for: fetih
---

# Detection Attacks on Scada Systems


## Ne Zaman Kullanılır

- Dağıt:ing yaparken intrusion Tespit capabilities in a SCADA environment for the first time
- investigating yaparken suspected cyber attacks against industrial control systems
- building yaparken Tespit rules for OT-specific attack patterns (Stuxnet, TRITON, Industroyer)
- integrating yaparken OT network monitoring with an enterprise SOC for unified threat visibility
- responding yaparken to alerts from OT security monitoring tools (Dragos, Nozomi, Claroty)

**Kullanma:** for Tespit etme attacks on IT-only networks without SCADA/ICS components, for building generic network IDS rules (see building-Tespit-rules-with-sigma), or for incident response procedures after an attack is confirmed (see performing-ot-incident-response).

## Ön Gereksinimler

- Passive network monitoring sensors Dağıtılmış on SPAN/TAP ports at OT network boundaries
- OT intrusion Tespit system (Dragos Platform, Nozomi Guardian, Claroty xDome, or Suricata with OT rulesets)
- Understanding of industrial protocols in use (Modbus, DNP3, OPC UA, EtherNet/IP, S7comm)
- Baseline of normal SCADA communication patterns (polling intervals, function codes, register ranges)
- Erişim: process historian data for physical process anomaly correlation

## İş Akışı

### Adım 1: Establish SCADA Communication Baselines

Before Tespit etme anomalies, establish what normal SCADA traffic looks like. Industrial protocols are highly deterministic - the same master polls the same slaves at the same intervals reading the same registers.

```python
#!/usr/bin/env python3
"""SCADA Communication Baseline Builder.

Analyzes OT network traffic to establish deterministic baselines for
Modbus/TCP, DNP3, EtherNet/IP, and S7comm communications.
"""

import json
import sys
from collections import defaultdict
from datetime import datetime
from statistics import mean, stdev

try:
    from scapy.all import rdpcap, IP, TCP, UDP
except ImportError:
    print("Install scapy: pip install scapy")
    sys.exit(1)

MODBUS_FUNC_NAMES = {
    1: "Read Coils", 2: "Read Discrete Inputs",
    3: "Read Holding Registers", 4: "Read Input Registers",
    5: "Write Single Coil", 6: "Write Single Register",
    8: "Diagnostics", 15: "Write Multiple Coils",
    16: "Write Multiple Registers", 17: "Report Slave ID",
    22: "Mask Write Register", 23: "Read/Write Multiple Registers",
    43: "Encapsulated Interface Transport",
}


class SCADABaselineBuilder:
    """Builds deterministic baselines from SCADA traffic captures."""

    def __init__(self):
        self.modbus_sessions = defaultdict(lambda: {
            "func_codes": defaultdict(int),
            "register_ranges": set(),
            "intervals": [],
            "last_seen": None,
            "request_count": 0,
        })
        self.communication_pairs = defaultdict(lambda: {
            "protocols": set(),
            "packet_count": 0,
            "first_seen": None,
            "last_seen": None,
        })

    def process_pcap(self, pcap_file):
        """Process pcap file to build SCADA baselines."""
        packets = rdpcap(pcap_file)
        print(f"[*] Processing {len(packets)} packets for baseline...")

        for pkt in packets:
            if not pkt.haslayer(IP):
                continue

            src = pkt[IP].src
            dst = pkt[IP].dst
            ts = float(pkt.time)

            # Track communication pairs
            pair_key = f"{src}->{dst}"
            pair = self.communication_pairs[pair_key]
            pair["packet_count"] += 1
            if pair["first_seen"] is None:
                pair["first_seen"] = ts
            pair["last_seen"] = ts

            # Analyze Modbus/TCP
            if pkt.haslayer(TCP) and pkt[TCP].dport == 502:
                self._analyze_modbus(pkt, src, dst, ts)

    def _analyze_modbus(self, pkt, src, dst, timestamp):
        """Extract Modbus function codes and register ranges."""
        payload = bytes(pkt[TCP].payload)
        if len(payload) < 8:
            return

        # MBAP header: transaction_id(2) + protocol_id(2) + length(2) + unit_id(1) + func_code(1)
        func_code = payload[7]
        session_key = f"{src}->{dst}"
        session = self.modbus_sessions[session_key]

        session["func_codes"][func_code] += 1
        session["request_count"] += 1
        session["protocols"] = {"Modbus/TCP"}

        # Track polling intervals
        if session["last_seen"] is not None:
            interval = timestamp - session["last_seen"]
            if 0.01 < interval < 60:  # Reasonable polling interval
                session["intervals"].append(interval)
        session["last_seen"] = timestamp

        # Extract register range for read/write operations
        if len(payload) >= 12 and func_code in (1, 2, 3, 4, 5, 6, 15, 16):
            start_register = (payload[8] << 8) | payload[9]
            if func_code in (1, 2, 3, 4, 15, 16) and len(payload) >= 12:
                count = (payload[10] << 8) | payload[11]
                session["register_ranges"].add((func_code, start_register, start_register + count))

    def generate_baseline(self):
        """Generate the baseline profile from collected data."""
        baseline = {
            "generated": datetime.now().isoformat(),
            "modbus_baselines": {},
            "communication_pairs": {},
        }

        for session_key, session in self.modbus_sessions.items():
            avg_interval = mean(session["intervals"]) if session["intervals"] else 0
            interval_std = stdev(session["intervals"]) if len(session["intervals"]) > 1 else 0

            baseline["modbus_baselines"][session_key] = {
                "allowed_function_codes": list(session["func_codes"].keys()),
                "function_code_distribution": {
                    MODBUS_FUNC_NAMES.get(k, f"FC{k}"): v
                    for k, v in session["func_codes"].items()
                },
                "polling_interval_avg_sec": round(avg_interval, 3),
                "polling_interval_stddev": round(interval_std, 3),
                "register_ranges": [
                    {"func_code": r[0], "start": r[1], "end": r[2]}
                    for r in session["register_ranges"]
                ],
                "total_requests": session["request_count"],
            }

        return baseline

    def export_baseline(self, output_file):
        """Export baseline to JSON file."""
        baseline = self.generate_baseline()
        with open(output_file, "w") as f:
            json.dump(baseline, f, indent=2)
        print(f"[*] Baseline saved to: {output_file}")

        # Print summary
        print(f"\n{'='*60}")
        print("SCADA COMMUNICATION BASELINE SUMMARY")
        print(f"{'='*60}")
        for session, data in baseline["modbus_baselines"].items():
            print(f"\n  Session: {session}")
            print(f"    Function Codes: {data['allowed_function_codes']}")
            print(f"    Polling Interval: {data['polling_interval_avg_sec']}s (+/- {data['polling_interval_stddev']}s)")
            print(f"    Register Ranges: {len(data['register_ranges'])}")
            print(f"    Total Requests: {data['total_requests']}")


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python scada_baseline.py <pcap_file> [output.json]")
        sys.exit(1)

    builder = SCADABaselineBuilder()
    builder.process_pcap(sys.argv[1])
    output = sys.argv[2] if len(sys.argv) > 2 else "scada_baseline.json"
    builder.export_baseline(output)
```

### Adım 2: Dağıt: OT-Specific Detection Rules

Create Tespit rules for known SCADA attack patterns including those used by TRITON, Industroyer/CrashOverride, and PIPEDREAM/INCONTROLLER.

```yaml


alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-tespit etme
Modbus write from unauthorized source";
  modbus_func:!read_coils; modbus_func:!read_discrete_inputs;
  modbus_func:!read_holding_registers; modbus_func:!read_input_registers;
  flow:to_server,established;
  threshold:type both, track by_src, count 1, seconds 60;
  classtype:attempted-admin;
  sid:3000001; rev:1;
)

alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-tespit etme
Modbus diagnostics command to PLC";
  modbus_func:diagnostics;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000002; rev:1;
)

alert modbus any any -> $OT_PLC_SUBNET 502 (
  msg:"OT-CRITICAL Modbus broadcast write command";
  modbus_unit_id:0;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000003; rev:1;
  priority:1;
)


alert tcp any any -> $SIEMENS_PLC_SUBNET 102 (
  msg:"OT-CRITICAL S7comm CPU STOP command Detected";
  content:"|03 00|"; offset:0; depth:2;
  content:"|29|"; offset:17; depth:1;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000010; rev:1;
  priority:1;
)

alert tcp any any -> $SIEMENS_PLC_SUBNET 102 (
  msg:"OT-CRITICAL S7comm program download to PLC";
  content:"|03 00|"; offset:0; depth:2;
  content:"|1a|"; offset:17; depth:1;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000011; rev:1;
  priority:1;
)


alert tcp any any -> $OT_RTU_SUBNET 20000 (
  msg:"OT-CRITICAL DNP3 cold restart command";
  content:"|05 64|"; offset:0; depth:2;
  content:"|0d|"; offset:12; depth:1;
  flow:to_server,established;
  classtype:attempted-dos;
  sid:3000020; rev:1;
  priority:1;
)

alert tcp any any -> $OT_RTU_SUBNET 20000 (
  msg:"OT-CRITICAL DNP3 file transfer / firmware update";
  content:"|05 64|"; offset:0; depth:2;
  content:"|19|"; offset:12; depth:1;
  flow:to_server,established;
  classtype:attempted-admin;
  sid:3000021; rev:1;
  priority:1;
)


alert ip !$AUTHORIZED_OT_HOSTS any -> $OT_PLC_SUBNET any (
  msg:"OT-tespit etme
Unauthorized device communicating with PLC subnet";
  flow:to_server;
  threshold:type limit, track by_src, count 1, seconds 3600;
  classtype:network-scan;
  sid:3000030; rev:1;
)

alert tcp any any -> $OT_NETWORK any (
  msg:"OT-tespit etme
Port scan targeting industrial protocols";
  flags:S;
  threshold:type threshold, track by_src, count 10, seconds 60;
  classtype:network-scan;
  sid:3000031; rev:1;
)
```

### Adım 3: Implement Process Data Anomaly Tespit

Monitor physical process data from the historian to tespit etme
attacks that manipulate the process while hiding their effects from operators (the Stuxnet attack pattern).

```python
#!/usr/bin/env python3
"""SCADA Process Data Anomaly tespit etme (or).

Monitors historian data to tespit etme
physical process anomalies
that may indicate cyber attacks manipulating control logic
while spoofing sensor readings (Stuxnet-style attacks).
"""

import json
import sys
import time
from collections import deque
from dataclasses import dataclass
from datetime import datetime
from statistics import mean, stdev
from typing import Optional

try:
    import requests
except ImportError:
    print("Install requests: pip install requests")
    sys.exit(1)


@dataclass
class ProcessVariable:
    """Represents a monitored process variable."""
    tag_name: str
    description: str
    unit: str
    low_limit: float
    high_limit: float
    rate_of_change_limit: float  # Maximum change per second
    engineering_low: float
    engineering_high: float


@dataclass
class Anomaly:
    """Represents a Detected process anomaly."""
    timestamp: str
    tag_name: str
    anomaly_type: str
    severity: str
    current_value: float
    expected_range: str
    description: str
    attack_pattern: str = ""


class ProcessAnomalyDetector:
    """tespit etme (s) anomalies in SCADA process data from historian."""

    def __init__(self, historian_url, api_key=None):
        self.historian_url = historian_url
        self.api_key = api_key
        self.variables = {}
        self.history = defaultdict(lambda: deque(maxlen=1000))
        self.anomalies = []

    def add_variable(self, var: ProcessVariable):
        """Register a process variable to monitor."""
        self.variables[var.tag_name] = var

    def fetch_current_values(self):
        """Fetch current values from historian API."""
        headers = {}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"

        tag_list = list(self.variables.keys())
        params = {"tags": ",".join(tag_list), "count": 1}

        try:
            resp = requests.get(
                f"{self.historian_url}/api/v1/streams/values/current",
                params=params,
                headers=headers,
                timeout=10,
                verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true",  # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
            )
            resp.raise_for_status()
            return resp.json()
        except requests.RequestException as e:
            print(f"[ERROR] Historian API error: {e}")
            return {}

    def check_value(self, tag_name, value, timestamp):
        """Check a process variable value against all Tespit rules."""
        var = self.variables.get(tag_name)
        if not var:
            return

        self.history[tag_name].append((timestamp, value))

        # Rule 1: Value out of engineering limits
        if value < var.engineering_low or value > var.engineering_high:
            self.anomalies.append(Anomaly(
                timestamp=timestamp,
                tag_name=tag_name,
                anomaly_type="OUT_OF_RANGE",
                severity="critical",
                current_value=value,
                expected_range=f"{var.engineering_low}-{var.engineering_high} {var.unit}",
                description=f"{tag_name} ({var.description}) at {value} {var.unit} - outside engineering limits",
                attack_pattern="Process manipulation - value driven outside safe operating range",
            ))

        # Rule 2: Rate of change exceeds physical limits
        history = list(self.history[tag_name])
        if len(history) >= 2:
            prev_ts, prev_val = history[-2]
            try:
                dt = (datetime.fromisoformat(timestamp) - datetime.fromisoformat(prev_ts)).total_seconds()
                if dt > 0:
                    rate = abs(value - prev_val) / dt
                    if rate > var.rate_of_change_limit:
                        self.anomalies.append(Anomaly(
                            timestamp=timestamp,
                            tag_name=tag_name,
                            anomaly_type="RATE_OF_CHANGE_VIOLATION",
                            severity="high",
                            current_value=value,
                            expected_range=f"Max rate: {var.rate_of_change_limit} {var.unit}/s",
                            description=(
                                f"{tag_name} changing at {rate:.2f} {var.unit}/s "
                                f"(limit: {var.rate_of_change_limit} {var.unit}/s)"
                            ),
                            attack_pattern="Possible sensor spoofing or actuator manipulation",
                        ))
            except (ValueError, TypeError):
                pass

        # Rule 3: Flatline Tespit (sensor reading not changing when process is active)
        if len(history) >= 20:
            recent_values = [v for _, v in list(history)[-20:]]
            if len(set(recent_values)) == 1:
                self.anomalies.append(Anomaly(
                    timestamp=timestamp,
                    tag_name=tag_name,
                    anomaly_type="FLATLINE_DetectED",
                    severity="high",
                    current_value=value,
                    expected_range="Expected variation during active process",
                    description=f"{tag_name} flatlined at {value} for 20+ consecutive readings",
                    attack_pattern="Stuxnet-style replay attack - frozen sensor value while process is manipulated",
                ))

        # Rule 4: Statistical anomaly (z-score based)
        if len(history) >= 50:
            values = [v for _, v in list(history)[-50:]]
            avg = mean(values)
            std = stdev(values) if len(values) > 1 else 0
            if std > 0:
                z_score = abs(value - avg) / std
                if z_score > 3.5:
                    self.anomalies.append(Anomaly(
                        timestamp=timestamp,
                        tag_name=tag_name,
                        anomaly_type="STATISTICAL_ANOMALY",
                        severity="medium",
                        current_value=value,
                        expected_range=f"Mean: {avg:.2f}, StdDev: {std:.2f} (z={z_score:.1f})",
                        description=f"{tag_name} value {value} is {z_score:.1f} standard deviations from mean",
                        attack_pattern="Possible gradual process manipulation",
                    ))

    def report_anomalies(self):
        """Print Detected anomalies."""
        if not self.anomalies:
            print("[*] No anomalies Detected")
            return

        print(f"\n{'='*70}")
        print(f"PROCESS ANOMALY tespit etme (ION) REPORT - {len(self.anomalies)} anomalies")
        print(f"{'='*70}")

        for a in self.anomalies:
            print(f"\n  [{a.severity.upper()}] {a.anomaly_type}")
            print(f"    Time: {a.timestamp}")
            print(f"    Tag: {a.tag_name}")
            print(f"    Value: {a.current_value}")
            print(f"    Expected: {a.expected_range}")
            print(f"    Detail: {a.description}")
            if a.attack_pattern:
                print(f"    Attack Pattern: {a.attack_pattern}")


if __name__ == "__main__":
    from collections import defaultdict

    tespit etme (or) = ProcessAnomalyDetector(
        historian_url="https://10.30.1.50:5450",
    )

    # Define monitored process variables for a chemical reactor
    tespit etme (or).add_variable(ProcessVariable(
        tag_name="REACTOR_01.TEMP",
        description="Reactor 1 Temperature",
        unit="C",
        low_limit=150, high_limit=280,
        rate_of_change_limit=5.0,
        engineering_low=100, engineering_high=350,
    ))
    tespit etme (or).add_variable(ProcessVariable(
        tag_name="REACTOR_01.PRESSURE",
        description="Reactor 1 Pressure",
        unit="bar",
        low_limit=2.0, high_limit=8.0,
        rate_of_change_limit=0.5,
        engineering_low=0, engineering_high=12.0,
    ))
    tespit etme (or).add_variable(ProcessVariable(
        tag_name="PUMP_03.FLOW",
        description="Feed Pump 3 Flow Rate",
        unit="m3/h",
        low_limit=5.0, high_limit=25.0,
        rate_of_change_limit=2.0,
        engineering_low=0, engineering_high=30.0,
    ))

    print("[*] Starting process anomaly monitoring...")
    print("[*] Press Ctrl+C to stop and generate report")

    try:
        while True:
            data = tespit etme (or).fetch_current_values()
            for item in data.get("items", []):
                tespit etme (or).check_value(
                    item.get("tag"),
                    item.get("value"),
                    item.get("timestamp", datetime.now().isoformat()),
                )
            time.sleep(5)
    except KeyboardInterrupt:
        tespit etme (or).report_anomalies()
```

### Adım 4: tespit etme
Known ICS Malware Indicators

Monitor for indicators of compromise (IOCs) associated with known ICS-targeting malware families.

```yaml

malware_families:
  TRITON_TRISIS:
    description: "Targets Schneider Electric Triconex Safety Instrumented Systems"
    target: "Safety controllers (SIS)"
    network_indicators:
      - protocol: "TriStation"
        port: 1502
        pattern: "Unusual TriStation commands from non-engineering workstation"
      - protocol: "TCP"
        pattern: "Connection to Triconex controller from unauthorized IP"
    host_indicators:
      - "trilog.exe present on engineering workstation"
      - "inject.bin in System32 directory"
      - "imain.bin payload targeting Triconex firmware"
    Tespit_rule: |
      alert tcp !$SIS_ENGINEERING_WS any -> $SIS_CONTROLLERS 1502 (
        msg:"OT-CRITICAL Unauthorized TriStation connection to SIS";
        flow:to_server; sid:3000100; rev:1; priority:1;)

  INDUSTROYER_CRASHOVERRIDE:
    description: "Targets power grid SCADA via IEC 60870-5-101/104, IEC 61850, OPC DA"
    target: "Power grid substations and SCADA"
    network_indicators:
      - protocol: "IEC 60870-5-104"
        port: 2404
        pattern: "Rapid sequence of control commands outside normal polling"
      - protocol: "OPC DA"
        pattern: "Enumeration of OPC servers followed by write commands"
    host_indicators:
      - "haslo.exe (backdoor launcher)"
      - "61850.dll (IEC 61850 attack module)"
      - "OPC.dll (OPC DA attack module)"
      - "104.dll (IEC 104 attack module)"
    Tespit_rule: |
      alert tcp any any -> $SUBSTATION_RTU 2404 (
        msg:"OT-CRITICAL Rapid IEC 104 control commands - Industroyer pattern";
        flow:to_server,established;
        threshold:type threshold, track by_src, count 50, seconds 10;
        sid:3000110; rev:1; priority:1;)

  PIPEDREAM_INCONTROLLER:
    description: "Modular ICS attack framework targeting Schneider/OMRON PLCs and OPC UA"
    target: "Multiple PLC vendors (Schneider, OMRON) and OPC UA servers"
    network_indicators:
      - protocol: "CODESYS"
        port: 1217
        pattern: "CODESYS runtime exploitation attempts"
      - protocol: "OPC UA"
        port: 4840
        pattern: "OPC UA server enumeration and unauthorized method calls"
      - protocol: "Modbus"
        port: 502
        pattern: "Rapid Modbus write commands to multiple unit IDs"
    host_indicators:
      - "TAGRUN tool for OPC UA scanning"
      - "CODECALL tool for CODESYS exploitation"
      - "OMSHELL tool for OMRON PLC interaction"
    Tespit_rule: |
      alert tcp any any -> $OT_NETWORK 1217 (
        msg:"OT-CRITICAL CODESYS runtime connection - PIPEDREAM indicator";
        flow:to_server,established;
        sid:3000120; rev:1; priority:1;)
```

## Key Concepts

| Term | Definition |
|------|------------|
| SCADA | Supervisory Control and Data Acquisition - architecture for remote monitoring and control of industrial processes via RTUs and communication infrastructure |
| IDS/IPS for OT | Intrusion Tespit/Prevention Systems designed for industrial protocols, using both signature-based and anomaly-based Tespit methods |
| Process Anomaly | Deviation in physical process behavior (temperature, pressure, flow) that may indicate cyber manipulation of control systems |
| Man-in-the-Middle (MITM) | Attack intercepting communication between SCADA master and field devices to modify commands or spoof sensor readings |
| Replay Attack | Capturing legitimate SCADA traffic and replaying it to mask malicious changes to the process (used by Stuxnet) |
| Protocol Anomaly | Deviation from expected industrial protocol behavior including unauthorized function codes, unusual polling patterns, or command sequences |

## Tools & Systems

- **Dragos Platform**: OT cybersecurity platform with threat Tespit powered by Dragos threat intelligence on ICS-targeting activity groups
- **Nozomi Networks Guardian**: OT/IoT visibility and threat Tespit using asset intelligence, anomaly Tespit, and vulnerability assessment
- **Claroty xDome**: Cyber-physical systems protection with continuous threat monitoring and alert prioritization
- **Suricata with ET Open ICS rules**: Open-source IDS/IPS with community-maintained rules for industrial protocol Tespit
- **Zeek (Bro) with OT scripts**: Network security monitor with protocol analyzers for Modbus, DNP3, and BACnet

## Common Scenarios

### Scenario: Tespit etme TRITON-Style Attack on Safety Systems

**Context**: An OT security monitoring system alerts on unusual TriStation protocol traffic to a Triconex safety controller from an IP address that is not the authorized SIS engineering workstation.

**Approach**:
1. Immediately verify the source IP of the TriStation traffic - is it the authorized SIS engineering workstation or a compromised host?
2. Check if there is an authorized maintenance activity scheduled for the SIS controllers
3. Capture full packet payload of the TriStation communication for forensic analysis
4. Alert the process safety team - SIS compromise is a safety-critical event
5. If unauthorized, isolate the source host from the network immediately
6. Verify SIS controller logic integrity by comparing running logic against known-good backup
7. Check all engineering workstations in the facility for TRITON indicators (trilog.exe, inject.bin)

**Pitfalls**: Never assume SIS traffic anomalies are false positives - TRITON demonstrated that sophisticated attackers specifically target safety systems. Do not restart the SIS controller without first verifying firmware and logic integrity. Avoid alerting only the IT SOC; the process safety team must be immediately engaged for any SIS-related incident.

## Output Format

```
SCADA Attack Tespit Report
===============================
Tespit Time: YYYY-MM-DD HH:MM:SS UTC
Tespit Source: [IDS/Anomaly tespit etme (or)/Process Monitor]

ALERT DETAILS:
  Alert ID: [unique identifier]
  Severity: Critical/High/Medium/Low
  Attack Category: [Protocol Anomaly/Process Manipulation/Unauthorized Access]
  MITRE ATT&CK for ICS: [Technique ID and name]

  Source: [IP/hostname]
  Target: [IP/hostname - device type]
  Protocol: [Modbus/DNP3/S7comm/etc]
  Detail: [Specific Bul:ing description]

BASELINE COMPARISON:
  Normal: [Expected behavior]
  Observed: [Actual behavior that triggered alert]
  Deviation: [How the observed differs from baseline]

RECOMMENDED RESPONSE:
  1. [Immediate containment action]
  2. [Verification step]
  3. [Escalation path]
```

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