International Conference on Computer Safety, Reliability, and Security最新文献

Constraint-Guided Test Execution Scheduling: An Experience Report at ABB Robotics 约束引导测试执行调度:ABB机器人的经验报告

International Conference on Computer Safety, Reliability, and Security Pub Date : 2023-06-02 DOI: 10.48550/arXiv.2306.01529

A. Gotlieb, Morten Mossige, Helge Spieker

{"title":"Constraint-Guided Test Execution Scheduling: An Experience Report at ABB Robotics","authors":"A. Gotlieb, Morten Mossige, Helge Spieker","doi":"10.48550/arXiv.2306.01529","DOIUrl":"https://doi.org/10.48550/arXiv.2306.01529","url":null,"abstract":"Automated test execution scheduling is crucial in modern software development environments, where components are frequently updated with changes that impact their integration with hardware systems. Building test schedules, which focus on the right tests and make optimal use of the available resources, both time and hardware, under consideration of vast requirements on the selection of test cases and their assignment to certain test execution machines, is a complex optimization task. Manual solutions are time-consuming and often error-prone. Furthermore, when software and hardware components and test scripts are frequently added, removed or updated, static test execution scheduling is no longer feasible and the motivation for automation taking care of dynamic changes grows. Since 2012, our work has focused on transferring technology based on constraint programming for automating the testing of industrial robotic systems at ABB Robotics. After having successfully transferred constraint satisfaction models dedicated to test case generation, we present the results of a project called DynTest whose goal is to automate the scheduling of test execution from a large test repository, on distinct industrial robots. This paper reports on our experience and lessons learned for successfully transferring constraint-based optimization models for test execution scheduling at ABB Robotics. Our experience underlines the benefits of a close collaboration between industry and academia for both parties.","PeriodicalId":402659,"journal":{"name":"International Conference on Computer Safety, Reliability, and Security","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115074864","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}

引用次数: 0

Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection 基于参数的激光注入嵌入式神经网络攻击评估

International Conference on Computer Safety, Reliability, and Security Pub Date : 2023-04-25 DOI: 10.48550/arXiv.2304.12876

Mathieu Dumont, Kevin Hector, Pierre-Alain Moëllic, J. Dutertre, S. Pontié

{"title":"Evaluation of Parameter-based Attacks against Embedded Neural Networks with Laser Injection","authors":"Mathieu Dumont, Kevin Hector, Pierre-Alain Moëllic, J. Dutertre, S. Pontié","doi":"10.48550/arXiv.2304.12876","DOIUrl":"https://doi.org/10.48550/arXiv.2304.12876","url":null,"abstract":"Upcoming certification actions related to the security of machine learning (ML) based systems raise major evaluation challenges that are amplified by the large-scale deployment of models in many hardware platforms. Until recently, most of research works focused on API-based attacks that consider a ML model as a pure algorithmic abstraction. However, new implementation-based threats have been revealed, emphasizing the urgency to propose both practical and simulation-based methods to properly evaluate the robustness of models. A major concern is parameter-based attacks (such as the Bit-Flip Attack, BFA) that highlight the lack of robustness of typical deep neural network models when confronted by accurate and optimal alterations of their internal parameters stored in memory. Setting in a security testing purpose, this work practically reports, for the first time, a successful variant of the BFA on a 32-bit Cortex-M microcontroller using laser fault injection. It is a standard fault injection means for security evaluation, that enables to inject spatially and temporally accurate faults. To avoid unrealistic brute-force strategies, we show how simulations help selecting the most sensitive set of bits from the parameters taking into account the laser fault model.","PeriodicalId":402659,"journal":{"name":"International Conference on Computer Safety, Reliability, and Security","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116134217","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}

引用次数: 1

Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception 在安全关键感知中处理数据驱动模型运行时不确定性的体系结构模式

International Conference on Computer Safety, Reliability, and Security Pub Date : 2022-06-14 DOI: 10.48550/arXiv.2206.06838

Janek Groß, R. Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch

{"title":"Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception","authors":"Janek Groß, R. Adler, Michael Kläs, Jan Reich, Lisa Jöckel, Roman Gansch","doi":"10.48550/arXiv.2206.06838","DOIUrl":"https://doi.org/10.48550/arXiv.2206.06838","url":null,"abstract":"Data-driven models (DDM) based on machine learning and other AI techniques play an important role in the perception of increasingly autonomous systems. Due to the merely implicit definition of their behavior mainly based on the data used for training, DDM outputs are subject to uncertainty. This poses a challenge with respect to the realization of safety-critical perception tasks by means of DDMs. A promising approach to tackling this challenge is to estimate the uncertainty in the current situation during operation and adapt the system behavior accordingly. In previous work, we focused on runtime estimation of uncertainty and discussed approaches for handling uncertainty estimations. In this paper, we present additional architectural patterns for handling uncertainty. Furthermore, we evaluate the four patterns qualitatively and quantitatively with respect to safety and performance gains. For the quantitative evaluation, we consider a distance controller for vehicle platooning where performance gains are measured by considering how much the distance can be reduced in different operational situations. We conclude that the consideration of context information of the driving situation makes it possible to accept more or less uncertainty depending on the inherent risk of the situation, which results in performance gains.","PeriodicalId":402659,"journal":{"name":"International Conference on Computer Safety, Reliability, and Security","volume":"2 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122589396","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}

引用次数: 3

STPA-driven Multilevel Runtime Monitoring for In-time Hazard Detection stpa驱动的多级别运行时监控，用于实时危险检测

International Conference on Computer Safety, Reliability, and Security Pub Date : 2022-04-19 DOI: 10.48550/arXiv.2204.08999

S. Gautham, Georgios Bakirtzis, Alexander Will, A. Jayakumar, C. Elks

引用次数: 4

Evaluation Framework for Performance Limitation of Autonomous Systems under Sensor Attack 传感器攻击下自主系统性能限制评估框架

International Conference on Computer Safety, Reliability, and Security Pub Date : 2021-03-12 DOI: 10.1007/978-3-030-83903-1_1

Koichi Shimizu, D. Suzuki, Ryosuke Muramatsu, H. Mori, Tomoyuki Nagatsuka, Tsutomu Matsumoto

引用次数: 1

DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers DeepCert:神经网络图像分类器的上下文相关鲁棒性验证

International Conference on Computer Safety, Reliability, and Security Pub Date : 2021-03-02 DOI: 10.1007/978-3-030-83903-1_5

Colin Paterson, Haoze Wu, John M. Grese, R. Calinescu, C. Pasareanu, Clark W. Barrett

引用次数: 12

SASSI: Safety Analysis Using Simulation-Based Situation Coverage for Cobot Systems SASSI:基于模拟情境覆盖的协作机器人系统安全分析

International Conference on Computer Safety, Reliability, and Security Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-83903-1_13

Benjamin Lesage, R. Alexander

引用次数: 7

A Compositional Semantics for Repairable BDMPs 可修bdmp的组合语义

International Conference on Computer Safety, Reliability, and Security Pub Date : 2020-09-15 DOI: 10.1007/978-3-030-54549-9_6

Shahid Khan, J. Katoen, M. Bouissou

引用次数: 3

Securing Electric Vehicle Charging Systems Through Component Binding 通过组件绑定保护电动汽车充电系统

International Conference on Computer Safety, Reliability, and Security Pub Date : 2020-09-15 DOI: 10.1007/978-3-030-54549-9_26

A. Fuchs, Dustin Kern, C. Krauß, M. Zhdanova

引用次数: 8

Towards an Automated Exploration of Secure IoT/CPS Design-Variants 迈向安全物联网/CPS设计变体的自动化探索

International Conference on Computer Safety, Reliability, and Security Pub Date : 2020-09-15 DOI: 10.1007/978-3-030-54549-9_25

Lukas Gressl, Michael Krisper, C. Steger, U. Neffe

引用次数: 1