A Machine Learning Approach for Combating Cyber Attacks in Self-Driving Vehicles

被引：1

作者：

Berry, Hunter ^{[1
]}

Abdel-Malek, Mai A. ^{[2
]}

Ibrahim, Ahmed S. ^{[2
]}

机构：

[1] Berry Coll, Polit Sci & Cyber Secur Law Dept, Mt Berry, GA 30149 USA

[2] Florida Int Univ, Dept Elect & Comp Engn, Miami, FL 33199 USA

来源：

SOUTHEASTCON 2021 | 2021年

基金：

美国国家科学基金会;

关键词：

Cyber-security; machine learning; self-driving vehicles; gradient boosting;

D O I：

10.1109/SOUTHEASTCON45413.2021.9401856

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Self-driving vehicles are very susceptible to cyber attacks. This paper aims to utilize a machine learning approach in combating cyber attacks on self-driving vehicles. We focus on detecting incorrect data that are injected into the data bus of vehicles. We will utilize the extreme gradient boosting approach, as a promising example of machine learning, to classify such incorrect information. We will discuss in details the research methodology, which includes acquiring the driving data, pre-processing it, artificially inserting incorrect information, and finally classifying it. Our results show that the considered algorithm achieve accuracy of up to 92% in detecting the abnormal behavior on the car data bus.

引用

页码：741 / 743

页数：3