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.DAT to .DXF Converter

NACA 2412 Airfoil Databases are in .dat file. The problem? There are no clean .dat -> .dxf converters. The solution? I built one. This post is directly linked to my ongoing project at the time of writing: Comparative…

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NACA 2412 Airfoil Databases are in .dat file. The problem? There are no clean .dat -> .dxf converters. The solution? I built one.









This post is directly linked to my ongoing project at the time of writing: Comparative Aerodynamic Analysis Of Wing Planforms.






I needed to model a NACA 2412 wing which had .dat coordinate files available. But OnShape needed .dxf in order to import the coordinates. I couldn't find a clean converter online, so I decided to build one for the sake of this project. I wanted to share the script:




#plot_airfoil.py
# Desired chord length (mm)
CHORD = 200
# import necessary libraries for dxf conversion
import ezdxf
# Path to airfoil data file
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
filename = os.path.join(script_dir, "..", "airfoil", "naca2412.dat")
#Open the file
with open(filename, 'r') as file:
lines = file.readlines()
#Print every line
#for line in lines:
# print(line)
import matplotlib.pyplot as plt
x = []
y = []
with open(filename, 'r') as file:
lines = file.readlines()
for line in lines[1:]: #Skip the NACA 2412 header line
values = line.split()
if len(values) == 2:
x_coord = float(values[0]) * CHORD
y_coord = float(values[1]) * CHORD
x.append(float(x_coord))
y.append(float(y_coord))
plt.figure(figsize=(8, 3))
plt.plot(x, y)

plt.axis("equal")
plt.grid(True)

plt.xlabel("x")
plt.ylabel("y")
plt.title("NACA 2412 Airfoil")

#plt.show()
# Remove the hashtag from the line above to plot the airfoil. The line is commented out to avoid opening a plot window when running the script in a non-interactive environment.
# ---
# Export airfoil to dxf file
CHORD = 200.0 #mm
#Scale coordinates to desired chord length
scaled_x = x
scaled_y = y

doc = ezdxf.new()
msp = doc.modelspace()
#Add polyline
msp.add_lwpolyline(list(zip(scaled_x, scaled_y)), close=True)
#Save file
script_dir = os.path.dirname(os.path.abspath(__file__))
output_file = os.path.join(script_dir, "..", "cad", "naca2412_200mm.dxf")

doc.saveas(output_file)

print(f"Saved to: {output_file}")






The paths & directories in the code was based on my own folder structure, so do feel free to change it as per your requirement if you do plan on using it! It is also available on the GitHub repository mentioned above.

1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Vulnerability Remediation & Verification
Syntax validiert (0 Fehler)
title: Detect Exploitation - .DAT to .DXF Converter
id: 1ed293e0-86dc-43ce-a766-c7f7b45b7d2e
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
Syntax validiert (0 Fehler)
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = ".DAT to .DXF Converter" ascii wide
    condition:
        any of them
}
Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("DAT to DXF Converter")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count
Syntax validiert (0 Fehler)
message: "*DAT to DXF Converter*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "DAT to DXF Converter"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

2. Cyber Threat Intelligence & Forensik

🎯
MITRE ATT&CK Matrix Navigator 14 Taktiken
Reconnaissance
-
Resource Development
-
Initial Access
Execution
Persistence
-
Privilege Escalation
Defense Evasion
Credential Access
-
Discovery
-
Lateral Movement
-
Collection
-
Command and Control
Exfiltration
-
Impact
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich .DAT to .DXF Converter.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

⚡ Empfohlene Sofortmaßnahmen
  • 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
  • 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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