2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)最新文献

{"title":"Neuron-PUF: Physical Unclonable Function Based on a Single Spiking Neuron","authors":"M. Elshamy, H. Stratigopoulos","doi":"10.1109/IOLTS52814.2021.9486716","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486716","url":null,"abstract":"We propose a novel Physical Unclonable Function (PUF) concept based on a single spiking neuron. The inherent variability of the neuron results in a chip-unique analog spiking pattern that is digitized to produce a chip-unique digital key. A stability booster is also employed based on self-masking to obtain a fully stable digital key. The PUF is area and power effective since a single PUF cell is used to produce an arbitrarily sized digital key. We demonstrate PUF quality metrics close to ideal values and argue that the PUF is resilient against various physical attacks.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117108795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Title page]","authors":"","doi":"10.1109/iolts52814.2021.9486681","DOIUrl":"https://doi.org/10.1109/iolts52814.2021.9486681","url":null,"abstract":"","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"57 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127421084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flip Flop Weighting: A technique for estimation of safety metrics in Automotive Designs","authors":"F. A. D. Silva, A. Bagbaba, S. Hamdioui, C. Sauer","doi":"10.1109/IOLTS52814.2021.9486697","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486697","url":null,"abstract":"The requirements of ISO26262 for the development of safety-critical Integrated Circuits (IC) demand substantial efforts on fault analysis for safety metrics evaluation. Failing to achieve the required conditions entails modifications to the circuit, additional iterations through critical design phases, and consequently extra costs and delays. For that reason, providing accurate methods to estimate safety metrics is of great importance. This paper proposes a methodology that can efficiently and precisely estimate the safety metrics of Automotive designs. The technique is based on the characterization of a netlist to determine how hardware components contribute to fault propagation. Also, by examining the test stimuli applied during simulation, we can rank Workloads/Testbenches according to their fault detection coverage. The approach was verified running fault injection campaigns on distinct gate-level hardware designs, including an Automotive CPU. Our results show that the fault detection coverage can be estimated with an average error rate of 3% at up to 20X faster execution times when compared to the traditional campaigns. Hence the methodology provides an efficient and cost-effective mechanism to support engineers in a confident design space exploration.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"31 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TAURUM P2T: Advanced Secure CAN-FD Architecture for Road Vehicle","authors":"F. Oberti, Ernesto Sánchez, A. Savino, Filippo Parisi, S. Carlo","doi":"10.1109/IOLTS52814.2021.9486688","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486688","url":null,"abstract":"Interconnected devices are growing very fast in today's automotive market, providing new and complex features that cover very different domains. This vast and continuous requirement for new features brings to impact areas categorized as real-time safety-critical devices, opening the possibility to add potential vulnerabilities. By analyzing the security vulnerabilities within vehicle networks, this paper aims at proposing a new generation of a secure architecture based on Controller Area Network (CAN) called TAURUM P2T. This new architecture looks at mitigating the vulnerabilities found in the current network systems of road vehicles by introducing a low-cost and efficient solution based on the introduction of a Secure CAN network able to implement a novel key provisioning strategy. The proposed architecture has been implemented, resorting to a commercial Multi-Protocol Vehicle Interface module, and the obtained results experimentally demonstrate the approach's feasibility.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"69 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120876737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive-HMD: Accurate and Cost-Efficient Machine Learning-Driven Malware Detection using Microarchitectural Events","authors":"Yifeng Gao, Hosein Mohammadi Makrani, Mehrdad Aliasgari, Amin Rezaei, Jessica Lin, H. Homayoun, H. Sayadi","doi":"10.1109/IOLTS52814.2021.9486701","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486701","url":null,"abstract":"To address the high complexity and computational overheads of conventional software-based detection techniques, Hardware Malware Detection (HMD) has shown promising results as an alternative anomaly detection solution. HMD methods apply Machine Learning (ML) classifiers on microarchitectural events monitored by built-in Hardware Performance Counter (HPC) registers available in modern microprocessors to recognize the patterns of anomalies (e.g., signatures of malicious applications). Existing hardware malware detection solutions have mainly focused on utilizing standard ML algorithms to detect the existence of malware without considering an adaptive and cost-efficient approach for online malware detection. Our comprehensive analysis across a wide range of malicious software applications and different branches of machine learning algorithms indicates that the type of adopted ML algorithm to detect malicious applications at the hardware level highly correlates with the type of the examined malware, and the ultimate performance evaluation metric (F-measure, robustness, latency, detection rate/cost, etc.) to select the most efficient ML model for distinguishing the target malware from benign program. Therefore, in this work we propose Adaptive-HMD, an accurate and cost-efficient machine learning-driven framework for online malware detection using low-level microarchitectural events collected from HPC registers. Adaptive-HMD is equipped with a lightweight tree-based decision-making algorithm that accurately selects the most efficient ML model to be used for the inference in online malware detection according to the users' preference and optimal performance vs. cost (hardware overhead and latency) criteria. The experimental results demonstrate that Adaptive-HMD achieves up to 94% detection rate (F-measure) while improving the cost-efficiency of ML-based malware detection by more than 5X as compared to existing ensemble-based malware detection methods.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128416221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Blank page]","authors":"","doi":"10.1109/iolts52814.2021.9486680","DOIUrl":"https://doi.org/10.1109/iolts52814.2021.9486680","url":null,"abstract":"","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128663557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Copyright notice]","authors":"","doi":"10.1109/iolts52814.2021.9486717","DOIUrl":"https://doi.org/10.1109/iolts52814.2021.9486717","url":null,"abstract":"","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115813609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Anomalous Behavior Detection Method Based on Power Analysis Utilizing Steady State Power Waveform Predicted by LSTM","authors":"Kazunari Takasaki, Ryoichi Kida, N. Togawa","doi":"10.1109/IOLTS52814.2021.9486706","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486706","url":null,"abstract":"Hardware security issues have emerged in recent years as Internet of Things (IoT) devices have rapidly spread. Power analysis is one of the methods to detect anomalous operations, but it is hard to apply it to IoT devices where an operating system and various software programs are running and hence its power waveforms become more complex. In this paper, we propose an anomalous behavior detection method utilizing application-specific power behaviors extracted by steady-state power waveform, which is generated by LSTM (long short-term memory). The proposed method is based on extracting application-specific power behaviors by predicting steady-state power waveforms. At that time, by using LSTM, we can effectively predict steady-state power waveforms, even if they include one or more cycled waveforms and/or they are composed of many complex waveforms. In the experiment, we implement three normal application programs and one anomalous application program on a single board computer and apply the proposed method to it. The experimental results demonstrate that the proposed method successfully detects the anomalous power behavior of an anomalous application program, while the existing method cannot.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent Test of Reconfigurable Scan Networks for Self-Aware Systems","authors":"Chih-Hao Wang, N. Lylina, A. Atteya, Tong-Yu Hsieh, H. Wunderlich","doi":"10.1109/IOLTS52814.2021.9486710","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486710","url":null,"abstract":"Self-aware and safety-critical hardware/software systems rely on a variety of embedded instruments, sensors, monitors and design-for-test circuitry to check the system integrity. The access to these internal instruments is supported by standards commonly called iJTAG and employs so called reconfigurable scan networks (RSNs), which are more and more used at runtime, too. They collect periodically and also concurrently the information on the circuit's health state and deliver it to some dependability management unit. The integrity of RSNs is essential for the dependability of self-aware systems and can be ensured by a combination of periodic and concurrent test methods of the RSN itself. The paper at hand presents the first concurrent online test method for RSNs by adding a brief integrity test to each access operation. The presented scheme includes a hardware extension of negligible size, supports offline test, diagnosis and post-silicon validation as well, and is further referred as ROSTI: RSN Online/Offline Self-Test Infrastructure. It exploits the original RSN control signals and does not require any modification of the underlying RSN. The hardware costs are independent of the size of the RSN, and ROSTI is flexible for generating different test sequences for different types of faults. The experimental results validate these characteristics and show that ROSTI is highly scalable.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"600 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127790831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Suitability Analysis of Software Based Testing Strategies for the On-line Testing of Artificial Neural Networks Applications in Embedded Devices","authors":"A. Ruospo, D. Piumatti, A. Floridia, Ernesto Sánchez","doi":"10.1109/IOLTS52814.2021.9486704","DOIUrl":"https://doi.org/10.1109/IOLTS52814.2021.9486704","url":null,"abstract":"Electronic devices based on artificial intelligence solutions are pervading our everyday life. Nowadays, human decision processes are supported by real-time data gathered from intelligent systems. Artificial Neural Networks (ANNs) are one of the most used deep learning predictive models due to their outstanding computational capabilities. However, assessing their reliability is still an open issue faced by both the academic and industrial worlds, especially when ANNs are deployed on safety-critical systems, such as self-driving cars in the automotive world. In these systems, a strategy for identifying hardware faults is required by industry standards (e.g., ISO26262 for automotive, and DO254 for avionics). Among the existing in-field test strategies, the periodic scheduling of on-line Software Test Library (STL) is a wide strategy adopted; STL allows to reach an acceptable fault coverage without the need for additional hardware. However, when dealing with ANN-based applications, the execution of on-line tests interleaving the ANN inferences may jeopardise the strive for performance maximization. The paper presents a comprehensive analysis of six possible scenarios concerning the execution of on-line self-test programs in embedded devices running ANN-based applications. In the proposed scenarios, the impact of the STL execution on the ANN performance is analyzed; in particular, the execution times of an inference and the Fault Detection Time (FDT) of the STL are discussed and compared. Experimental analyses are provided by relying on: an open-source RISC-V platform running two different convolutional neural networks; a STL for RISC-V cores with a maximum achievable fault coverage of 90%.","PeriodicalId":185642,"journal":{"name":"2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116787423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}