论文标题
汽车以太网网络中AVTP的无监督网络入侵检测系统
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks
论文作者
论文摘要
网络入侵检测系统（NIDSS）被广泛认为是为了保护多种网络攻击的车辆内网络的有效工具。但是，由于网络攻击总是在发展，因此不再采用基于签名的入侵检测系统。另一种解决方案可以是部署基于深度学习的入侵检测系统，该系统在检测网络流量中未知的攻击模式中起着重要作用。因此，在本文中，我们比较了不同无监督和机器学习基于机器的异常检测算法的性能，以实时检测音频视频传输协议（AVTP），这是在最新的基于AutoMotive Ethernet的车辆中实现的应用程序层协议（AVTP）。在最近发表的“汽车以太网入侵数据集”上进行的数值结果表明，深度学习模型在不同实验环境下的机器学习中明显超过其他最先进的传统异常检测模型。
Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system which play an important role in detecting unknown attack patterns in network traffic. Hence, in this paper, we compare the performance of different unsupervised deep and machine learning based anomaly detection algorithms, for real-time detection of anomalies on the Audio Video Transport Protocol (AVTP), an application layer protocol implemented in the recent Automotive Ethernet based in-vehicle network. The numerical results, conducted on the recently published "Automotive Ethernet Intrusion Dataset", show that deep learning models significantly outperfom other state-of-the art traditional anomaly detection models in machine learning under different experimental settings.
