In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.
{"title":"Cyberattacks on Adaptive Cruise Controls and Emergency Braking Systems: Adversary Models, Impact Assessment, and Countermeasures","authors":"Adriana Berdich;Bogdan Groza","doi":"10.1109/TR.2024.3373810","DOIUrl":"10.1109/TR.2024.3373810","url":null,"abstract":"In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 2","pages":"1216-1230"},"PeriodicalIF":5.9,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaodong Gou, Ao Zhang, Chengguang Wang, Yan Liu, Xue Zhao, Shunkun Yang
{"title":"Software Fault Localization Based on Network Spectrum and Graph Neural Network","authors":"Xiaodong Gou, Ao Zhang, Chengguang Wang, Yan Liu, Xue Zhao, Shunkun Yang","doi":"10.1109/tr.2024.3374410","DOIUrl":"https://doi.org/10.1109/tr.2024.3374410","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"11 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accelerated life testing (ALT) experiments are widely used in reliability studies on extremely durable products having large mean times to failure. Simple step-stress ALT (SSALT) is a special class of ALT that tests the units under investigation on two different conditions by changing the stress factor (e.g., temperature, voltage, or pressure) at a predetermined time point of the experiment. In this study, we propose the maximum product of spacings (MPS) technique for estimating the unknown lifetime parameters as an alternative to the maximum likelihood (ML), which in some cases is not possible to be used. The MPS estimator is defined for a simple SSALT model under Type-II censoring and proved to be asymptotically equivalent to the corresponding ML estimator. The specific case of Weibull lifetimes sharing a common shape parameter on both stress levels under the tampered failure rate assumption is considered in more detail. Existence and uniqueness results are shown for the point estimators of both methods and an adjusted bootstrap algorithm is suggested for constructing interval inference procedures. Further, the ML and MPS approaches are compared via a simulation study and applied to two real lifetime data examples.
加速寿命试验（ALT）广泛应用于对平均失效时间较长的耐用产品进行可靠性研究。简单阶跃应力加速寿命测试（SSALT）是一种特殊的加速寿命测试，它通过在实验的预定时间点改变应力因子（如温度、电压或压力），在两种不同的条件下对被测单元进行测试。在本研究中，我们提出了最大间距积（MPS）技术，用于估计未知寿命参数，以替代在某些情况下无法使用的最大似然法（ML）。MPS 估计器是为 II 型普查下的简单 SSALT 模型定义的，并证明其与相应的 ML 估计器在渐近上是等效的。更详细地考虑了在篡改失效率假设下，两个应力水平上的 Weibull 寿命具有共同形状参数的特定情况。结果显示了两种方法的点估计器的存在性和唯一性，并提出了一种用于构建区间推断程序的调整自举算法。此外，还通过模拟研究对 ML 和 MPS 方法进行了比较，并将其应用于两个实际寿命数据实例。
{"title":"Product of Spacings Estimation in Step-Stress Accelerated Life Testing: An Alternative to Maximum Likelihood","authors":"Maria Kateri;Nikolay I. Nikolov","doi":"10.1109/TR.2024.3369977","DOIUrl":"10.1109/TR.2024.3369977","url":null,"abstract":"Accelerated life testing (ALT) experiments are widely used in reliability studies on extremely durable products having large mean times to failure. Simple step-stress ALT (SSALT) is a special class of ALT that tests the units under investigation on two different conditions by changing the stress factor (e.g., temperature, voltage, or pressure) at a predetermined time point of the experiment. In this study, we propose the maximum product of spacings (MPS) technique for estimating the unknown lifetime parameters as an alternative to the maximum likelihood (ML), which in some cases is not possible to be used. The MPS estimator is defined for a simple SSALT model under Type-II censoring and proved to be asymptotically equivalent to the corresponding ML estimator. The specific case of Weibull lifetimes sharing a common shape parameter on both stress levels under the tampered failure rate assumption is considered in more detail. Existence and uniqueness results are shown for the point estimators of both methods and an adjusted bootstrap algorithm is suggested for constructing interval inference procedures. Further, the ML and MPS approaches are compared via a simulation study and applied to two real lifetime data examples.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1433-1445"},"PeriodicalIF":5.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huixiong Wang, Xing Pan, Zeqing Liu, Yuheng Dang, Dongpao Hong
{"title":"A Framework for the Network-Based Assessment of System Dynamic Resilience","authors":"Huixiong Wang, Xing Pan, Zeqing Liu, Yuheng Dang, Dongpao Hong","doi":"10.1109/tr.2024.3371215","DOIUrl":"https://doi.org/10.1109/tr.2024.3371215","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"107 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fault-Tolerant Communication in HSDC: Ensuring Reliable Data Transmission in Smart Cities","authors":"Hui Dong, Mengjie Lv, Weibei Fan","doi":"10.1109/tr.2024.3371953","DOIUrl":"https://doi.org/10.1109/tr.2024.3371953","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"24 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140155424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the strong uncertainty in the retrieval order of containers and the complex coupling relationship between container yard production equipment, it is challenging for yards to formulate an appropriate slot allocation strategy to control the proportion of rehandling containers. Meanwhile, the unpredictable performance of the slot allocation strategy results in the yard lacking the means to adjust the allocation strategy. To address these issues, an efficient container rehandling probability prediction model based on deep learning has been proposed to assist yards in formulating and adjusting slot allocation strategy. Moreover, we design a container slot allocation strategy driven by the predictive container rehandling probability for reducing the proportion of rehandling container at yards. Extensive experiments on the container storage dataset demonstrate that: 1) the prediction model based on deep learning enables to efficiently and precisely predict the container rehandling, 2) taking Seq2Seq network as the prediction layer of model outperforms other deep sequence models on MSE, MAE, and accuracy, and 3) the slot allocation strategy based on the predictive container rehandling probability can effectively reduce the probability of the rehandling container.
{"title":"Container Rehandling Probability Prediction Model Based on Seq2Seq Network","authors":"Guojie Chen;Weidong Zhao;Xianhui Liu;Mingyue Wei","doi":"10.1109/TR.2024.3392919","DOIUrl":"10.1109/TR.2024.3392919","url":null,"abstract":"Due to the strong uncertainty in the retrieval order of containers and the complex coupling relationship between container yard production equipment, it is challenging for yards to formulate an appropriate slot allocation strategy to control the proportion of rehandling containers. Meanwhile, the unpredictable performance of the slot allocation strategy results in the yard lacking the means to adjust the allocation strategy. To address these issues, an efficient container rehandling probability prediction model based on deep learning has been proposed to assist yards in formulating and adjusting slot allocation strategy. Moreover, we design a container slot allocation strategy driven by the predictive container rehandling probability for reducing the proportion of rehandling container at yards. Extensive experiments on the container storage dataset demonstrate that: 1) the prediction model based on deep learning enables to efficiently and precisely predict the container rehandling, 2) taking Seq2Seq network as the prediction layer of model outperforms other deep sequence models on MSE, MAE, and accuracy, and 3) the slot allocation strategy based on the predictive container rehandling probability can effectively reduce the probability of the rehandling container.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1569-1580"},"PeriodicalIF":5.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. The approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.
本文介绍了一种数据驱动方法，用于评估户外光伏（PV）组串逆变器的故障机制和可靠性退化。制造商标明的光伏逆变器使用寿命会因运行地点条件的影响而变化。为了解决加速测试、状态监测或退化建模在退化估计方面的局限性，我们提出了一种以机器学习（ML）为导向的方法。利用自 2016 年起运营的 1.4 兆瓦光伏电站的数据，我们采用无监督单类支持向量机 ML 技术来分析逆变器和传感器数据，能够将湿度循环和温度波动划分为主要故障机制。利用逆变器特有的异常警报关系和警报细节，光伏逆变器的输出水平被视为其可用性或可用可靠性。随后，一个连续的马尔可夫模型被应用到六个月的警报数据中，结果显示，在连续运行 20 年后，所述平均可靠性为 20%。这些结果支持对有时限的预防措施提出建议，以提高光伏逆变器在各种户外条件下的可靠性。该方法提供了一种无损、自上而下和通用的方法，可用于分析任何暴露在室外条件下的商用光伏逆变器，但这取决于相关数据的可用性。
{"title":"Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment","authors":"Sukanta Roy;Shahid Tufail;Mohd Tariq;Arif Sarwat","doi":"10.1109/TR.2024.3359540","DOIUrl":"10.1109/TR.2024.3359540","url":null,"abstract":"This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. The approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1418-1432"},"PeriodicalIF":5.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140155423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mustafa Tosun, Umut Can Cabuk, Elif Haytaoglu, Orhan Dagdeviren, Yusuf Ozturk
{"title":"DPkCR: Distributed Proactive k-Connectivity Recovery Algorithm for UAV-Based MANETs","authors":"Mustafa Tosun, Umut Can Cabuk, Elif Haytaoglu, Orhan Dagdeviren, Yusuf Ozturk","doi":"10.1109/tr.2024.3370743","DOIUrl":"https://doi.org/10.1109/tr.2024.3370743","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"3 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140128736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix Staudigl, Hazem Al Indari, Daniel Schön, Hsin-Yu Chen, Dominik Sisejkovic, Jan Moritz Joseph, Vikas Rana, Stephan Menzel, Amelie Hagelauer, Rainer Leupers
{"title":"It's Getting Hot in Here: Hardware Security Implications of Thermal Crosstalk on ReRAMs","authors":"Felix Staudigl, Hazem Al Indari, Daniel Schön, Hsin-Yu Chen, Dominik Sisejkovic, Jan Moritz Joseph, Vikas Rana, Stephan Menzel, Amelie Hagelauer, Rainer Leupers","doi":"10.1109/tr.2024.3371589","DOIUrl":"https://doi.org/10.1109/tr.2024.3371589","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"8 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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