A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

TL;DR

This paper analyzes machine learning models for intrusion detection in intelligent transport systems, emphasizing edge computing's role in enhancing security and proposing a trust-aware federated hybrid detection framework.

Contribution

It introduces a novel federated intrusion detection framework combining multiple ML models with trust-aware aggregation for ITS security.

Findings

Effective detection of threats using federated models

Improved security with trust-aware aggregation

Enhanced latency and bandwidth efficiency

Abstract

AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT, and advanced 5G for vehicle-to-everything (V2X) connectivity, AI at the edge computing nodes plays a crucial role in protecting against sophisticated threats, enabling URLLC (ultra-low-latency communications) for smart transport, and enhancing infrastructure capabilities and safety. This research applies edge computing to improve latency, bandwidth efficiency, and service responsiveness by moving processing closer to devices, gateways, and users. However, this shift also expands the cyberattack surface because edge nodes are distributed, heterogeneous, and often resource-constrained. The paper proposes a trust-aware federated hybrid intrusion detection framework in which a random forest, a decision tree, and a linear SVM network learn complementary traffic representations at each edge site, while a server performs trust-aware aggregation of local model updates.