论文标题
LCCDE：基于决策的合奏框架，用于在车辆互联网上进行入侵检测
LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles
论文作者
论文摘要
现代车辆，包括自动驾驶汽车和互联车辆，通过与其他车辆，智能设备和基础设施的连接和通信采用了越来越多的功能。但是，车辆互联网（IOV）的连通性不断增长，也增加了网络攻击的脆弱性。为了保护IOV系统免受网络威胁的侵害，使用机器学习（ML）方法开发了可以识别恶意网络攻击的入侵检测系统（IDS）。为了准确检测IOV网络中的各种攻击，我们提出了一个名为领导者类别和信心决策集合（LCCDE）的新颖集合IDS框架。它是通过确定每个类别或类型攻击类型的三种高级ML算法（XGBoost，LightGBM和Catboost）中表现最佳的ML模型来构建的。然后利用具有预测置信度值的班级领导模型对检测各种类型的网络攻击做出准确的决策。在两个公共IOV安全数据集（汽车黑客和CICIDS2017数据集）上进行的实验证明了拟议的LCCDE在车内和外部网络上的入侵检测有效性。
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.
