Recently,automotive intrusion detection systems(IDSs)have emerged as promising defense approaches to counter attacks on in-vehicle networks(IVNs).However,the effectiveness of IDSs relies heavily on the quality of the ...Recently,automotive intrusion detection systems(IDSs)have emerged as promising defense approaches to counter attacks on in-vehicle networks(IVNs).However,the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation.Despite the availability of several datasets for automotive IDSs,there has been a lack of comprehensive analysis focusing on assessing these datasets.This paper aims to address the need for dataset assessment in the context of automotive IDSs.It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs,to evaluate the quality of datasets.These metrics take into consideration various aspects such as dataset description,collection environment,and attack complexity.This paper evaluates eight commonly used datasets for automotive IDSs using the proposed metrics.The evaluation reveals biases in the datasets,particularly in terms of limited contexts and lack of diversity.Additionally,it highlights that the attacks in the datasets were mostly injected without considering normal behaviors,which poses challenges for training and evaluating machine learning-based IDSs.This paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive IDSs.The proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security applications.Finally,this paper presents the requirements for high-quality datasets,including the need for representativeness,diversity,and balance.展开更多
we have developed ferroelectric capacitor fabrication technique to realize low-voltage and high-density ferroelectric random access memory （FRAM）. High temperature deposited IrOxtop electrode reveals high crystallin...we have developed ferroelectric capacitor fabrication technique to realize low-voltage and high-density ferroelectric random access memory （FRAM）. High temperature deposited IrOxtop electrode reveals high crystalline quality which drastically reduces the degradation of ferroelectric film by preventing hydrogen diffusion into ferroelectric film. This improvement enables us to commercialize highly-reliable 1T 1C FRAM with memory density of 4 Mb or larger.展开更多
Transistor size is constantly being reduced to improve performance as well as power consumption. For the channel length to be reduced, the corresponding gate dielectric thickness should also be reduced. Unfortunately,...Transistor size is constantly being reduced to improve performance as well as power consumption. For the channel length to be reduced, the corresponding gate dielectric thickness should also be reduced. Unfortunately, graphene devices are more complicated due to an extra capacitance called quantum capacitance （CQ） which limits the effective gate dielectric reduction. In this work, we analyzed the effect of CQ on device-scaling issues by extracting it from scaling of the channel length of devices. In contrast to previous reports for metal-insulator- metal structures, a practical device structure was used in conjunction with direct radio-frequency field-effect transistor measurements to describe the graphene channels. In order to precisely extract device parameters, we reassessed the equivalent circuit, and concluded that the on-state model should in fact be used. By careful consideration of the underlap region, our device modeling was shown to be in good agreement with the experimental data. CQ contributions to equivalent oxide thickness were analyzed in detail for varying impurity concentrations in graphene. Finally, we were able to demonstrate that despite contributions from CQ, graphene＇s high mobility and low-voltage operation allows for ~raphene channels suitable for next generation transistors.展开更多
基金supported in part by the 2021 Autonomous Driving Development Innovation Project of the Ministry of Science and ICT,‘Development of Technology for Security and Ultra-High-Speed Integrity of the Next-Generation Internal Net-Work of Autonomous Vehicles’(No.2021-0-01348)and in part by the National Research Foundation of Korea(NRF)grant funded by the Korean Government Ministry of Science and ICT(MSIT)under Grant NRF-2021R1A2C2014428.
文摘Recently,automotive intrusion detection systems(IDSs)have emerged as promising defense approaches to counter attacks on in-vehicle networks(IVNs).However,the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation.Despite the availability of several datasets for automotive IDSs,there has been a lack of comprehensive analysis focusing on assessing these datasets.This paper aims to address the need for dataset assessment in the context of automotive IDSs.It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs,to evaluate the quality of datasets.These metrics take into consideration various aspects such as dataset description,collection environment,and attack complexity.This paper evaluates eight commonly used datasets for automotive IDSs using the proposed metrics.The evaluation reveals biases in the datasets,particularly in terms of limited contexts and lack of diversity.Additionally,it highlights that the attacks in the datasets were mostly injected without considering normal behaviors,which poses challenges for training and evaluating machine learning-based IDSs.This paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive IDSs.The proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security applications.Finally,this paper presents the requirements for high-quality datasets,including the need for representativeness,diversity,and balance.
文摘we have developed ferroelectric capacitor fabrication technique to realize low-voltage and high-density ferroelectric random access memory （FRAM）. High temperature deposited IrOxtop electrode reveals high crystalline quality which drastically reduces the degradation of ferroelectric film by preventing hydrogen diffusion into ferroelectric film. This improvement enables us to commercialize highly-reliable 1T 1C FRAM with memory density of 4 Mb or larger.
文摘Transistor size is constantly being reduced to improve performance as well as power consumption. For the channel length to be reduced, the corresponding gate dielectric thickness should also be reduced. Unfortunately, graphene devices are more complicated due to an extra capacitance called quantum capacitance （CQ） which limits the effective gate dielectric reduction. In this work, we analyzed the effect of CQ on device-scaling issues by extracting it from scaling of the channel length of devices. In contrast to previous reports for metal-insulator- metal structures, a practical device structure was used in conjunction with direct radio-frequency field-effect transistor measurements to describe the graphene channels. In order to precisely extract device parameters, we reassessed the equivalent circuit, and concluded that the on-state model should in fact be used. By careful consideration of the underlap region, our device modeling was shown to be in good agreement with the experimental data. CQ contributions to equivalent oxide thickness were analyzed in detail for varying impurity concentrations in graphene. Finally, we were able to demonstrate that despite contributions from CQ, graphene＇s high mobility and low-voltage operation allows for ~raphene channels suitable for next generation transistors.
