Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles

by   Sakib Mahmud Khan, et al.

Connected vehicles (CVs), because of the external connectivity with other CVs and connected infrastructure, are vulnerable to cyberattacks that can instantly compromise the safety of the vehicle itself and other connected vehicles and roadway infrastructure. One such cyberattack is the false information attack, where an external attacker injects inaccurate information into the connected vehicles and eventually can cause catastrophic consequences by compromising safety-critical applications like the forward collision warning. The occurrence and target of such attack events can be very dynamic, making real-time and near-real-time detection challenging. Change point models, can be used for real-time anomaly detection caused by the false information attack. In this paper, we have evaluated three change point-based statistical models; Expectation Maximization, Cumulative Summation, and Bayesian Online Change Point Algorithms for cyberattack detection in the CV data. Also, data-driven artificial intelligence (AI) models, which can be used to detect known and unknown underlying patterns in the dataset, have the potential of detecting a real-time anomaly in the CV data. We have used six AI models to detect false information attacks and compared the performance for detecting the attacks with our developed change point models. Our study shows that change points models performed better in real-time false information attack detection compared to the performance of the AI models. Change point models having the advantage of no training requirements can be a feasible and computationally efficient alternative to AI models for false information attack detection in connected vehicles.


page 3

page 13

page 14


Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks ...

Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment

Connected vehicle (CV) systems are cognizant of potential cyber attacks ...

Statistical learning for change point and anomaly detection in graphs

Complex systems which can be represented in the form of static and dynam...

Quickest Search for a Change Point

This paper considers a sequence of random variables generated according ...

CASAD: CAN-Aware Stealthy-Attack Detection for In-Vehicle Networks

Nowadays, vehicles have complex in-vehicle networks (IVNs) with millions...

Sequential Change-point Detection for High-dimensional and non-Euclidean Data

In many modern applications, high-dimensional/non-Euclidean data sequenc...