{"title":"The Jean-Claude Laprie Award","authors":"","doi":"10.1109/dsn.2019.00012","DOIUrl":"https://doi.org/10.1109/dsn.2019.00012","url":null,"abstract":"","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307088","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}
Zilong Zhao, Sophie Cerf, R. Birke, B. Robu, S. Bouchenak, Sonia Ben Mokhtar, L. Chen
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT and cloud, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels. The first layer of quality model filters the suspicious data, where the second layer of classification model detects the anomaly types. We specifically focus on two use cases, (i) detecting 10 classes of IoT attacks and (ii) predicting 4 classes of task failures of big data jobs. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%) and up to 83% for cloud task failures (i.e., +20%), under a significant percentage of altered anomaly labels. Index Terms—Unreliable Data; Anomaly Detection; Failures; Attacks; Machine Learning
{"title":"Robust Anomaly Detection on Unreliable Data","authors":"Zilong Zhao, Sophie Cerf, R. Birke, B. Robu, S. Bouchenak, Sonia Ben Mokhtar, L. Chen","doi":"10.1109/DSN.2019.00068","DOIUrl":"https://doi.org/10.1109/DSN.2019.00068","url":null,"abstract":"Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT and cloud, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels. The first layer of quality model filters the suspicious data, where the second layer of classification model detects the anomaly types. We specifically focus on two use cases, (i) detecting 10 classes of IoT attacks and (ii) predicting 4 classes of task failures of big data jobs. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%) and up to 83% for cloud task failures (i.e., +20%), under a significant percentage of altered anomaly labels. Index Terms—Unreliable Data; Anomaly Detection; Failures; Attacks; Machine Learning","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021810","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}
Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast predictive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.
{"title":"Fast Predictive Repair in Erasure-Coded Storage","authors":"Zhirong Shen, Xiaolu Li, P. Lee","doi":"10.1109/DSN.2019.00062","DOIUrl":"https://doi.org/10.1109/DSN.2019.00062","url":null,"abstract":"Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast predictive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116548350","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}
We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants K, D, not known to the processes, such that for every K consecutive messages sent in one direction, at least one is delivered within time D. The best previous implementation used messages of bounded size, but exponential in n, the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size O(n log n)
{"title":"An Eventually Perfect Failure Detector for Networks of Arbitrary Topology Connected with ADD Channels Using Time-To-Live Values","authors":"Karla Vargas, S. Rajsbaum","doi":"10.1109/DSN.2019.00038","DOIUrl":"https://doi.org/10.1109/DSN.2019.00038","url":null,"abstract":"We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants K, D, not known to the processes, such that for every K consecutive messages sent in one direction, at least one is delivered within time D. The best previous implementation used messages of bounded size, but exponential in n, the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size O(n log n)","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479324","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":"William C. Carter Award","authors":"","doi":"10.1109/dsn.2019.00011","DOIUrl":"https://doi.org/10.1109/dsn.2019.00011","url":null,"abstract":"","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133090603","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}
Jiaqi Peng, Feng Li, Bingchang Liu, Lili Xu, Binghong Liu, Kai Chen, Wei Huo
Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.
{"title":"1dVul: Discovering 1-Day Vulnerabilities through Binary Patches","authors":"Jiaqi Peng, Feng Li, Bingchang Liu, Lili Xu, Binghong Liu, Kai Chen, Wei Huo","doi":"10.1109/DSN.2019.00066","DOIUrl":"https://doi.org/10.1109/DSN.2019.00066","url":null,"abstract":"Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130498237","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}
Onur Zungur, Guillermo Suarez-Tangil, G. Stringhini, Manuel Egele
Companies adopt Bring Your Own Device (BYOD) policies extensively, for both convenience and cost management. The compelling way of putting private and business related applications (apps) on the same device leads to the widespread usage of employee owned devices to access sensitive company data and services. Such practices create a security risk as a legitimate app may send business-sensitive data to third party servers through detrimental app functions or packaged libraries. In this paper, we propose BorderPatrol, a system for extracting contextual data that businesses can leverage to enforce access control in BYOD-enabled corporate networks through fine-grained policies. BorderPatrol extracts contextual information, which is the stack trace of the app function that generated the network traffic, on provisioned user devices and transfers this data in IP headers to enforce desired policies at network routers. BorderPatrol provides a way to selectively prevent undesired functionalities, such as analytics activities or advertisements, and help enforce information dissemination policies of the company while leaving other functions of the app intact. Using 2,000 apps, we demonstrate that BorderPatrol is effective in preventing packets which originate from previously identified analytics and advertisement libraries from leaving the network premises. In addition, we show BorderPatrol's capability in selectively preventing undesirable app functions using case studies.
{"title":"BorderPatrol: Securing BYOD using Fine-Grained Contextual Information","authors":"Onur Zungur, Guillermo Suarez-Tangil, G. Stringhini, Manuel Egele","doi":"10.1109/DSN.2019.00054","DOIUrl":"https://doi.org/10.1109/DSN.2019.00054","url":null,"abstract":"Companies adopt Bring Your Own Device (BYOD) policies extensively, for both convenience and cost management. The compelling way of putting private and business related applications (apps) on the same device leads to the widespread usage of employee owned devices to access sensitive company data and services. Such practices create a security risk as a legitimate app may send business-sensitive data to third party servers through detrimental app functions or packaged libraries. In this paper, we propose BorderPatrol, a system for extracting contextual data that businesses can leverage to enforce access control in BYOD-enabled corporate networks through fine-grained policies. BorderPatrol extracts contextual information, which is the stack trace of the app function that generated the network traffic, on provisioned user devices and transfers this data in IP headers to enforce desired policies at network routers. BorderPatrol provides a way to selectively prevent undesired functionalities, such as analytics activities or advertisements, and help enforce information dissemination policies of the company while leaving other functions of the app intact. Using 2,000 apps, we demonstrate that BorderPatrol is effective in preventing packets which originate from previously identified analytics and advertisement libraries from leaving the network premises. In addition, we show BorderPatrol's capability in selectively preventing undesirable app functions using case studies.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691507","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}
Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful "0-day" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those "0-day" attacks to at least "n-day" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.
{"title":"Detecting \"0-Day\" Vulnerability: An Empirical Study of Secret Security Patch in OSS","authors":"Xinda Wang, Kun Sun, A. Batcheller, S. Jajodia","doi":"10.1109/DSN.2019.00056","DOIUrl":"https://doi.org/10.1109/DSN.2019.00056","url":null,"abstract":"Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful \"0-day\" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those \"0-day\" attacks to at least \"n-day\" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793114","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}
R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. Castelo‐Branco, P. Carvalho, H. Madeira
Our research explores a recent paradigm called Biofeedback Augmented Software Engineering (BASE) that introduces a strong new element in the software development process: the programmers' biofeedback. In this Practical Experience Report we present the results of an experiment to evaluate the possibility of using pupillography to gather biofeedback from the programmers. The idea is to use pupillography to get meta information about the programmers' cognitive and emotional states (stress, attention, mental effort level, cognitive overload,...) during code development to identify conditions that may precipitate programmers making bugs or bugs escaping human attention, and tag the corresponding code locations in the software under development to provide online warnings to the programmer or identify code snippets that will need more intensive testing. The experiments evaluate the use of pupillography as cognitive load predictor, compare the results with the mental effort perceived by programmers using NASATLX, and discuss different possibilities for the use of pupillography as biofeedback sensor in real software development scenarios.
{"title":"Pupillography as Indicator of Programmers' Mental Effort and Cognitive Overload","authors":"R. Couceiro, G. Duarte, J. Durães, J. Castelhano, C. Duarte, C. Teixeira, M. Castelo‐Branco, P. Carvalho, H. Madeira","doi":"10.1109/DSN.2019.00069","DOIUrl":"https://doi.org/10.1109/DSN.2019.00069","url":null,"abstract":"Our research explores a recent paradigm called Biofeedback Augmented Software Engineering (BASE) that introduces a strong new element in the software development process: the programmers' biofeedback. In this Practical Experience Report we present the results of an experiment to evaluate the possibility of using pupillography to gather biofeedback from the programmers. The idea is to use pupillography to get meta information about the programmers' cognitive and emotional states (stress, attention, mental effort level, cognitive overload,...) during code development to identify conditions that may precipitate programmers making bugs or bugs escaping human attention, and tag the corresponding code locations in the software under development to provide online warnings to the programmer or identify code snippets that will need more intensive testing. The experiments evaluate the use of pupillography as cognitive load predictor, compare the results with the mental effort perceived by programmers using NASATLX, and discuss different possibilities for the use of pupillography as biofeedback sensor in real software development scenarios.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116040808","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}
Athanasios Chatzidimitriou, Pablo Bodmann, G. Papadimitriou, D. Gizopoulos, P. Rech
Fault injection in early microarchitecture-level simulation CPU models and beam experiments on the final physical CPU chip are two established methodologies to access the soft error reliability of a microprocessor at different stages of its design flow. Beam experiments, on one hand, estimate the devices expected soft error rate in realistic physical conditions by exposing it to accelerated particles fluxes. Fault injection in microarchitectural models of the processor, on the other hand, provides deep insights on faults propagation through the entire system stack, including the operating system. Combining beam experiments and fault injection data can deliver deep insights about the devices expected reliability when deployed in the field. However, it is yet largely unclear if the fault injection error rates can be compared to those reported by beam experiments and how this comparison can lead to informed soft error protection decisions in early stages of the system design.
{"title":"Demystifying Soft Error Assessment Strategies on ARM CPUs: Microarchitectural Fault Injection vs. Neutron Beam Experiments","authors":"Athanasios Chatzidimitriou, Pablo Bodmann, G. Papadimitriou, D. Gizopoulos, P. Rech","doi":"10.1109/DSN.2019.00018","DOIUrl":"https://doi.org/10.1109/DSN.2019.00018","url":null,"abstract":"Fault injection in early microarchitecture-level simulation CPU models and beam experiments on the final physical CPU chip are two established methodologies to access the soft error reliability of a microprocessor at different stages of its design flow. Beam experiments, on one hand, estimate the devices expected soft error rate in realistic physical conditions by exposing it to accelerated particles fluxes. Fault injection in microarchitectural models of the processor, on the other hand, provides deep insights on faults propagation through the entire system stack, including the operating system. Combining beam experiments and fault injection data can deliver deep insights about the devices expected reliability when deployed in the field. However, it is yet largely unclear if the fault injection error rates can be compared to those reported by beam experiments and how this comparison can lead to informed soft error protection decisions in early stages of the system design.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121297198","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：