@inproceedings{562c3e7ef1e0449a8f588920916143a5,
title = "Analyzing Origins of Safety and Security Interactions Using Feared Events Trees and Multi-level Model",
abstract = "Existing approaches to analyzing safety and security are often limited to a standalone viewpoint and lack a comprehensive mapping of the propagation of concerns, including unwanted (feared events like faults, failures, hazards, and attacks) and wanted ones (e.g., requirements, properties) and their interplay across different granular system representations. We take this problem to a novel combination of the Fault and Attack Trees (FATs) as Feared Events-Properties Trees (FEPTs) and propose an approach for analyzing safety and security interactions considering a multi-level model. The multi-level model facilitates identifying safety- and security-related feared events and associated properties across different system representation levels, viz. system, sub-system, information, and component. Likewise, FEPT allows modeling and analyzing the inter-dependencies between the feared events and properties and their propagation across these levels. We illustrate the use of this approach in a simple and realistic case of trajectory planning in an intersection point scenario regarding autonomous Connected-Driving Vehicles (CDVs) to address the potential interactions between safety and security.",
keywords = "n/a OA procedure",
author = "Megha Quamara and Christina Kolb and Brahim Hamid",
note = "Funding Information: This work was partially supported by AISEC Project EP/T027037/1. Funding Information: Acknowledgments. Distribution statement “A” (approved for public release, distribution unlimited). This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), contract FA875020C0508. The views, opinions, or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. The authors wish to also acknowledge the partial support by the National Science Foundation (NSF) under Awards 1846524 and 2139982, the Office of Naval Research (ONR) under Award N00014-20-1-2258, the Defense Advanced Research Projects Agency (DARPA) under Award HR00112010003, and the Okawa Research Grant. Funding Information: This work was partially supported by AISEC Project EP/T027037/1.. Acknowledgements. We thank David Aspinall from the University of Edinburgh and a member of the AISEC Project EP/T027037/1 for his support. Funding Information: Acknowledgments. This study was sponsored by the National Key R&D Program of China (2020YFB1600400). Funding Information: This research was supported by TEACHING, a project funded by the EU Horizon 2020 research and innovation programme under GA n. 871385. Funding Information: Supported by the H2020 project TEACHING (n. 871385) - www.teaching-h2020.eu. Funding Information: Acknowledgements. We are grateful to the SAFECOMP organization committee and collaborators for their support in arranging SASSUR, especially to Erwin Schoitsch and Matthieu Roy as Workshop Chairs and to Friedemann Bitsch as Publication Chair. We also thank all the authors of the submitted papers for their interest in the workshop, and the program committee for its work. Finally, the workshop is supported by the 4DASafeOps (Sweden{\textquoteright}s Software Center), ET4CQPPAJ (Sweden{\textquoteright}s Software Center), iRel4.0 (H2020-ECSEL ref. 876659; MCIN/AEI ref. PCI2020-112240; NextGen.EU/PRTR), REBECCA (HORIZON-KDT ref. 101097224; MCIN/AEI ref. PCI2022-135043-2; NextGen.EU/PRTR), VALU3S (H2020-ECSEL ref. 876852; MCIN/AEI ref. PCI2020-112001; NextGen.EU/ PRTR), and ETHEREAL (MCIN/AEI ref. PID2020-115220RB-C21; ERDF) projects, and by the Ramon y Cajal Program (MCIN/AEI ref. RYC-2017-22836; ESF). Funding Information: Acknowledgements. This workshop is partially funded by ERC Consolidator grant 864075 CAESAR. Funding Information: Acknowledgements. Experiments presented in this paper were carried out using the Grid{\textquoteright}5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organisations (see https:// www.grid5000.fr). This work has been partially supported by MIAI@Grenoble Alpes, (ANR-19-P3IA-0003) and TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215. Funding Information: Acknowledgements. Part of the work presented in the workshop received funding from the EC (H2020/ECSEL Joint Undertaking) and the partners National Funding Authorities (“tri-partite”) through the projects SECREDAS (nr. 783119), Comp4Drones (nr. 826610), AI4CSM (nr. 101007326). The AIMS5.0 project. Funded by the HORIZON-KDT-JU-2022-1-IA, project no. 101112089 and national funding authorities of the partners. The project ADEX is funded by the national Austrian Research Promotion Agency FFG in the program “ICT for Future” (FFG, BMK Austria) (no. 880811). The TEACHING project is funded by the EU Horizon 2020 research and innovation programme under GA n.871385, the LABYRINTH2020 project was funded under GA 861696. This list does not claim to be complete, for further details check the papers. Funding Information: Acknowledgments. The research described in this paper has been supported by the project MAIA “Monitoraggio Attivo dell{\textquoteright}Infrastruttura” funded by MIUR PON 14-20 (id code ARS01_00353). Funding Information: Acknowledgments. This work has been supported by the French government under the “France 2030” program, as part of the SystemX Technological Research Institute. The AIMOS tool is also funded under the Horizon Europe TRUMPET project grant no. 101070038 and the European Defence Fund AINCEPTION project grant no. 101103385. Funding Information: Supported by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project) (2022JBXT003). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-40953-0_15",
language = "English",
isbn = "978-3-031-40952-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "176--187",
editor = "J{\'e}r{\'e}mie Guiochet and Stefano Tonetta and Erwin Schoitsch and Matthieu Roy and Friedemann Bitsch",
booktitle = "Computer Safety, Reliability, and Security",
address = "Germany",
}