A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems
Zawad Yalmie Sazid, Robert Abbas, Sasa Maric
TLDR
A trust-aware federated hybrid intrusion detection framework secures Intelligent Transport Systems using multiple ML models at edge nodes.
Key contributions
- Proposes a novel trust-aware federated hybrid intrusion detection framework for ITS.
- Integrates Random Forest, Decision Tree, and Linear SVM models at edge nodes for local learning.
- Employs a server for trust-aware aggregation of local model updates to enhance security.
Why it matters
Intelligent Transport Systems face growing cyber threats with edge computing. This paper offers a proactive, AI-powered federated framework for intrusion detection, enhancing security and safety in connected transportation. It addresses challenges of distributed, resource-constrained edge nodes.
Original 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.
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