Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security

TL;DR

This paper explores how hyperparameters in ensemble machine learning models influence the time and effectiveness of adversarial attacks in automotive security, aiming to enhance trustworthy AI in multi-domain defense scenarios.

Contribution

It analyzes the impact of key hyperparameters on attack timing in ensemble models and proposes optimization strategies to improve resilient AI systems for multi-domain operations.

Findings

Hyperparameters like number of trees affect attack time significantly.

RF and GB models are more sensitive to hyperparameter changes than XGB.

Optimizing hyperparameters can extend the attacker's window of opportunity.

Abstract

Multi-Domain Operations (MDOs) emphasize cross-domain defense against complex and synergistic threats, with civilian infrastructures like smart cities and Connected Autonomous Vehicles (CAVs) emerging as primary targets. As dual-use assets, CAVs are vulnerable to Multi-Surface Threats (MSTs), particularly from Adversarial Machine Learning (AML) which can simultaneously compromise multiple in-vehicle ML systems (e.g., Intrusion Detection Systems, Traffic Sign Recognition Systems). Therefore, this study investigates how key hyperparameters in Decision Tree-based ensemble models-Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB)-affect the time required for a Black-Box AML attack i.e. Zeroth Order Optimization (ZOO). Findings show that parameters like the number of trees or boosting rounds significantly influence attack execution time, with RF and GB being more sensitive than XGB. Adversarial Training (AT) time is also analyzed to assess the attacker's window of opportunity. By optimizing hyperparameters, this research supports Defensive Trustworthy AI (D-TAI) practices within MST scenarios and contributes to the development of resilient ML systems for civilian and military domains, aligned with Cyber Social Security framework in MDOs and Human-AI Multi-Domain Task Forces.