A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using Attention Mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

An Energy Efficient Routing Protocol for In-Vehicle Wireless Sensor Networks

In this paper, an advanced distributed energy-efficient clustering (ADEEC) protocol was proposed with the aim of balancing energy consumption across the nodes to achieve longer network lifetime for In-Vehicle Wireless Sensor Networks (IVWSNs). The algorithm changes the cluster head selection probability based on residual energy and location distribution of nodes. Then node associate with the cluster head with least communication cost and high residual energy. Simulation results show that ADEEC achieves longer stability period, network lifetime,and throughput than the other classical clustering algorithms.

In-Vehicle Network Injection Attacks Detection Based on Feature Selection and Classification

Detecting abnormal data generated from cyberattacks has emerged as a crucial approach for identifying security threats within in-vehicle networks.The transmission of information through in-vehicle networks needs to follow specific data for-mats and communication protocols regulations.Typically,statistical algorithms are employed to learn these variation rules and facilitate the identification of abnormal data.However,the effectiveness of anomaly detection outcomes often falls short when confronted with highly deceptive in-vehicle network attacks.In this study,seven representative classification algorithms are selected to detect common in-vehicle network attacks,and a comparative analysis is employed to identify the most suitable and favorable detection method.In consideration of the communication protocol characteristics of in-vehicle networks,an optimal convolutional neural network(CNN)detection algorithm is proposed that uses data field characteristics and classifier selection,and its comprehensive performance is tested.In addition,the concept of Hamming distance between two adjacent packets within the in-vehicle network is introduced,enabling the proposal of an enhanced CNN algorithm that achieves robust detection of challenging-to-identify abnormal data.This paper also presents the proposed CNN classifica-tion algorithm that effectively addresses the issue of high false negative rate(FNR)in abnormal data detection based on the timestamp feature of data packets.The experimental results validate the efficacy of the proposed abnormal data detection algorithm,highlighting its strong detection performance and its potential to provide an effective solution for safeguarding the security of in-vehicle network information.

基于IVNS-ASPSO算法激增需求下第四方物流网络设计

近年来各大电商平台的不定期优惠活动导致需求激增的现象出现,激增需求下的网络设计问题成为了第四方物流(4PL)运作模式下一个新的挑战性问题。针对这一问题,基于对激增需求的刻画,建立了激增需求下4PL网络设计的数学模型。由于该问题的NPhard特性,采用自适应策略粒子群算法进行求解。并针对粒子群算法后期局部搜索能力不足,提出了迭代变邻域搜索策略,进而提出了嵌入迭代变邻域搜索的自适应策略粒子群算法,最后采用三组不同规模的算例进行仿真实验,验证了所提算法的有效性和优越性。

基于注意力机制的CNN-LSTM网络车内CAN总线入侵检测技术

随着智能汽车功能的不断扩展和用户群体的增加,汽车自身的网络安全性问题也逐渐引起人们的重视.智能汽车的大量外部接口为攻击者提供了许多种入侵车内网络的机会,而由于车内网络本身没有任何抵御外部攻击的机制,攻击者可以很容易地通过外部接口接入车辆内部网络并操控车辆,引发严重的交通安全事故.目前针对车辆内部网络的入侵检测系统被认为是抵御车辆内部网络入侵的有效方法.提出一种基于带有注意力机制的卷积-长短期记忆神经网络算法进行车内控制器局域网(controller area network,CAN)总线入侵检测.该方法首先将CAN总线通信数据转化为图像,然后利用卷积神经网络提取其中特征,再通过带有注意力机制的长短期记忆神经网络判断CAN总线通信是否异常.实验结果证明提出的方法在各项指标中都有较好的表现,能够有效检测针对CAN总线的入侵.

新能源汽车车内实时以太网技术发展综述

汽车产业正朝智能化方向发展,对汽车电控系统的网络架构与通信能力提出了挑战。具有高带宽、高实时、高安全性和高可靠性的车内实时以太网技术作为下一代车载网络的发展方向,受到广泛关注。分析了现有国内外新能源汽车车载网络技术(In-vehicle Network,IVN)的发展现状,阐述了车载网络在带宽压力及高可靠性需求下面临的挑战,讨论了未来以太网技术结合汽车产业的发展趋势,最后指出进一步完善汽车电控架构设计,开发车载以太网产品,制订相关标准规范的必要性。

Review of In‑Vehicle Optical Fiber Communication Technology

With the continuous development of automotive intelligent networking and autonomous driving technologies,the number of in-vehicle electronic systems and applications is increasing rapidly.This change increases the amount of data to be transmitted in the vehicle and puts forward further requirements of higher speed and safety for in-vehicle communication.Traditional vehicle bus technologies are no longer sufficient to meet today’s high-speed transmission requirements,in which copper cables are used extensively,resulting in serious electromagnetic interference(EMI).Vehicle optical fiber communication technology,besides greatly improving the data transmission rate,has the advantages of anti-EMI,reducing cable space and vehicle mass.This paper first presents the motivation of applying vehicle optical fiber communication technology and reviews the development history of vehicle optical fiber communication technology.Then,the paper researches the devel-opment trend of automotive electrical and electronic architecture(EEA),from distributed EEA to domain centralized EEA and zone-oriented EEA.Based on the discussion of the development trend of automotive EEA,an EEA based on vehicle optical fiber communication technology is proposed.Finally,the key points and future directions of vehicle optical fiber communication technology research are highlighted,including vehicle multi-mode optical fiber technology,vehicle optical fiber network protocol,and topology.

Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles

Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system(MEMS)accelerometer,an MEMS gyroscope,an advanced RISC machines(ARM)CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteris tics of the net work and the measurement system are stu died and repor ted.