This repository contains research code and documentation related to adversarial machine learning attacks against autonomous vehicle and operational technology systems.

All adversarial attack simulations in this repository are conducted exclusively against:

• Synthetic datasets
• Virtual simulation environments (CARLA, ROS, SUMO, OMNeT++)
• Isolated research environments

No real vehicle systems, production infrastructure, live OT environments, or public networks are targeted at any point in this research.

Code and methodologies in this repository are intended for academic research and defensive security purposes only. Specifically:

• Understanding adversarial attack vectors to design better defenses
• Evaluating ML model robustness in safety-critical environments
• Contributing to the broader AV/OT cybersecurity research community

Misuse of any code or methodology in this repository to attack real systems is strictly prohibited.

If you identify a security issue in this repository's code:

1. Do not open a public GitHub issue
2. Email: lamontesmithpmp@gmail.com with subject line [SECURITY] av-ot-adversarial-ml-framework
3. Expected response time: 72 hours

This research is conducted in accordance with Walsh College Institutional Review Board (IRB) guidelines and ethical research standards for doctoral dissertation research.