利用不平衡汽车黑客数据的自动驾驶汽车互联网智能攻击检测框架

S. Alshathri, A. Sayed, E. E. Hemdan
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摘要

现代自动驾驶汽车互联网(IoVs)促进了自动驾驶汽车的发展,这些汽车可以相互之间以及与周围环境进行交互,促进了汽车、基础设施和外部环境之间的实时数据交换和通信。由于车辆网络和控制器局域网(CAN)协议缺乏安全程序,车辆容易受到入侵。一种常见的攻击类型是报文注入攻击,即在原始电子控制单元(ECU)中插入虚假报文,以欺骗它们或制造故障。因此,本文探讨了现代物联网系统中网络攻击检测这一紧迫问题,因为车辆与外部世界和相互之间的连接日益紧密,这就形成了一个巨大的攻击面。车载网络(尤其是 CAN 协议)的脆弱性使其容易受到消息注入等攻击,从而造成严重后果。为此,我们提出了一种智能入侵检测系统(IDS),利用机器学习技术检测各种威胁。然而,汽车黑客数据集固有的不平衡性是一个重大挑战,它可能导致攻击类型的错误分类。为了克服这一问题,我们采用了各种不平衡预处理技术,包括近失误(NearMiss)、随机过度采样(ROS)和 TomLinks,来预处理和处理不平衡数据。然后,采用各种机器学习(ML)技术,包括逻辑回归(LR)、线性判别分析(LDA)、Naive Bayes(NB)和 K-Nearest Neighbors(k-NN),在平衡数据上检测和预测攻击类型。我们使用一套全面的评估指标(包括准确率、精确度、F1_Score 和召回率)来评估这些技术的性能和功效。这表明了所建议的 IDS 在使用车辆黑客攻击的不平衡数据检测外部和车内车辆网络中的网络攻击方面的效果。使用 k-NN 和各种重采样技术,结果表明,与现有研究相比,建议的系统在汽车黑客攻击数据集测试中实现了 100% 的检测率,证明了我们的方法在保护现代汽车系统免受高级威胁方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intelligent Attack Detection Framework for the Internet of Autonomous Vehicles with Imbalanced Car Hacking Data
The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats.
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