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 veh...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.展开更多
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 an...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.展开更多
The types and quantities of volatile organic compounds (VOCs) inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spec...The types and quantities of volatile organic compounds (VOCs) inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer (TD-GC/MS). Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions (temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration). Static vehicle testing demonstrated that although the amount of total volatile organic compounds (TVOC) detected within each vehicle was relatively distinct (4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C), toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle's air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.展开更多
This paper presents embedded system design of the In-Vehicle System (IVS) for the European Union (EU) emergency call (eCall) system. The IVS transmitter modules are designed, developed and implemented on a field progr...This paper presents embedded system design of the In-Vehicle System (IVS) for the European Union (EU) emergency call (eCall) system. The IVS transmitter modules are designed, developed and implemented on a field programmable gate array (FPGA) device. The modules are simulated, synthesized, and optimized to be loaded on a reconfigurable device as a system-on-chip (SoC) for the IVS electronic device. All the modules of the transmitter are designed as a single embedded module. The bench-top test is completed for testing and verification of the developed modules. The hardware architecture and interfaces are discussed. The IVS signal processing time is analyzed for multiple frequencies. A range of appropriate frequency and two hardware interfaces are proposed. A state-of-the-art FPGA design is employed as a first implementation approach for the IVS prototyping platform. This work is used as an initial step to implement all the modules of the IVS on a single SoC chip.展开更多
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 ...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%.展开更多
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...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.展开更多
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 fo...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.展开更多
Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent...Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.展开更多
Modern automobiles are equipped with connectivity features to enhance the user's comfort.Bluetooth is one such communication technology that is used to pair a personal device with an automotive infotainment unit.U...Modern automobiles are equipped with connectivity features to enhance the user's comfort.Bluetooth is one such communication technology that is used to pair a personal device with an automotive infotainment unit.Upon pairing,the user could access the personal information on the phone through the automotive head unit with minimum distraction while driving.However,such connectivity introduces a possibility for privacy attacks.Hence,performing an in-depth analysis of the system with privacy constraints is extremely important to prevent unauthorized access to personal information.In this work,we perform a systematic analysis of the Bluetooth network of an automotive infotainment unit to exploit security and privacy-related vulnerabilities.We model the identifed threat with respect to privacy constraints of the system,emphasize the severity of attacks through a standardized rating metric and then provide potential countermeasures to prevent the attack.We perform System Theoretic Process Analysis for Privacy as a part of the systematic analysis and use the Common Vulnerability Scoring System to derive attack severity.The identifed vulnerabilities are due to design flaws and assumptions on Bluetooth protocol implementation on automotive infotainment systems.We then elicit the vulnerability by performing a privacy attack on the Automotive system in an actual vehicle.We use Android Open-Source Project to report our findings and propose defense strategies.展开更多
为了实现MOST50网络对睡眠唤醒功能的支持,提出了MOST网络的睡眠唤醒方案。该方案包括支持睡眠唤醒功能的网络节点的硬件设计,通过电气控制线(Electrical control line,ECL)脉冲时序实现的网络唤醒方案的设计,以及网络睡眠通过节点间消...为了实现MOST50网络对睡眠唤醒功能的支持,提出了MOST网络的睡眠唤醒方案。该方案包括支持睡眠唤醒功能的网络节点的硬件设计,通过电气控制线(Electrical control line,ECL)脉冲时序实现的网络唤醒方案的设计,以及网络睡眠通过节点间消息问答方式的实现。该方案的有效性已在MOST50网络的实际应用中获得了充分的验证。展开更多
基金supported by Chongqing Big Data Engineering Laboratory for Children,Chongqing Electronics Engineering Technology Research Center for Interactive Learning,Project of Science and Technology Research Program of Chongqing Education Commission of China. (No.KJZD-K201801601).
文摘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.
文摘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.
文摘The types and quantities of volatile organic compounds (VOCs) inside vehicles have been determined in one new vehicle and two old vehicles under static conditions using the Thermodesorber-Gas Chromatograph/Mass Spectrometer (TD-GC/MS). Air sampling and analysis was conducted under the requirement of USEPA Method TO-17. A room-size, environment test chamber was utilized to provide stable and accurate control of the required environmental conditions (temperature, humidity, horizontal and vertical airflow velocity, and background VOCs concentration). Static vehicle testing demonstrated that although the amount of total volatile organic compounds (TVOC) detected within each vehicle was relatively distinct (4940 μg/m^3 in the new vehicle A, 1240 μg/m^3 in used vehicle B, and 132 μg/m^3 in used vehicle C), toluene, xylene, some aromatic compounds, and various C7-C12 alkanes were among the predominant VOC species in all three vehicles tested. In addition, tetramethyl succinonitrile, possibly derived from foam cushions was detected in vehicle B. The types and quantities of VOCs varied considerably according to various kinds of factors, such as, vehicle age, vehicle model, temperature, air exchange rate, and environment airflow velocity. For example, if the airflow velocity increases from 0.1 m/s to 0.7 m/s, the vehicle's air exchange rate increases from 0.15 h^-1 to 0.67 h^-1, and in-vehicle TVOC concentration decreases from 1780 to 1201 μg/m^3.
文摘This paper presents embedded system design of the In-Vehicle System (IVS) for the European Union (EU) emergency call (eCall) system. The IVS transmitter modules are designed, developed and implemented on a field programmable gate array (FPGA) device. The modules are simulated, synthesized, and optimized to be loaded on a reconfigurable device as a system-on-chip (SoC) for the IVS electronic device. All the modules of the transmitter are designed as a single embedded module. The bench-top test is completed for testing and verification of the developed modules. The hardware architecture and interfaces are discussed. The IVS signal processing time is analyzed for multiple frequencies. A range of appropriate frequency and two hardware interfaces are proposed. A state-of-the-art FPGA design is employed as a first implementation approach for the IVS prototyping platform. This work is used as an initial step to implement all the modules of the IVS on a single SoC chip.
基金This research was funded by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019 /0000867by the Ministry of Education of the Czech Republic, Project No. SP2021/32.
文摘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%.
文摘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.
基金supported by the the Young Scientists Fund of the National Natural Science Foundation of China under Grant 52102447by the Research Fund Project of Beijing Information Science&Technology University under Grant 2023XJJ33.
文摘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.
文摘Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.
文摘Modern automobiles are equipped with connectivity features to enhance the user's comfort.Bluetooth is one such communication technology that is used to pair a personal device with an automotive infotainment unit.Upon pairing,the user could access the personal information on the phone through the automotive head unit with minimum distraction while driving.However,such connectivity introduces a possibility for privacy attacks.Hence,performing an in-depth analysis of the system with privacy constraints is extremely important to prevent unauthorized access to personal information.In this work,we perform a systematic analysis of the Bluetooth network of an automotive infotainment unit to exploit security and privacy-related vulnerabilities.We model the identifed threat with respect to privacy constraints of the system,emphasize the severity of attacks through a standardized rating metric and then provide potential countermeasures to prevent the attack.We perform System Theoretic Process Analysis for Privacy as a part of the systematic analysis and use the Common Vulnerability Scoring System to derive attack severity.The identifed vulnerabilities are due to design flaws and assumptions on Bluetooth protocol implementation on automotive infotainment systems.We then elicit the vulnerability by performing a privacy attack on the Automotive system in an actual vehicle.We use Android Open-Source Project to report our findings and propose defense strategies.
文摘为了实现MOST50网络对睡眠唤醒功能的支持,提出了MOST网络的睡眠唤醒方案。该方案包括支持睡眠唤醒功能的网络节点的硬件设计,通过电气控制线(Electrical control line,ECL)脉冲时序实现的网络唤醒方案的设计,以及网络睡眠通过节点间消息问答方式的实现。该方案的有效性已在MOST50网络的实际应用中获得了充分的验证。