TY - CHAP A1 - Bergler, Matthias A1 - Tolvanen, Juha-Pekka A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - Social Engineering Exploits in Automotive Software Security BT - Modeling Human-targeted Attacks with SAM T2 - Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021) N2 - Security cannot be implemented into a system retrospectively without considerable effort, so security must be taken into consideration already at the beginning of the system development. The engineering of automotive software is by no means an exception to this rule. For addressing automotive security, the AUTOSAR and EAST-ADL standards for domain-specific system and component modeling provide the central foundation as a start. The EASTADL extension SAM enables fully integrated security modeling for traditional feature-targeted attacks. Due to the COVID-19 pandemic, the number of cyber-attacks has increased tremendously and of these, about 98 percent are based on social engineering attacks. These social engineering attacks exploit vulnerabilities in human behaviors, rather than vulnerabilities in a system, to inflict damage. And these social engineering attacks also play a relevant but nonetheless regularly neglected role for automotive software. The contribution of this paper is a novel modeling concept for social engineering attacks and their criticality assessment integrated into a general automotive software security modeling approach. This makes it possible to relate social engineering exploits with feature-related attacks. To elevate the practical usage, we implemented an integration of this concept into the established, domain-specific modeling tool MetaEdit+. The tool support enables collaboration between stakeholders, calculates vulnerability scores, and enables the specification of security objectives and measures to eliminate vulnerabilities. KW - automotive systems, social engineering attacks, design, model-based development, modeling, security Y1 - 2021 U6 - https://doi.org/10.3850/978-981-18-2016-8_720-cd SP - 2502 EP - 2509 PB - Research Publishing Services CY - Singapore ER - TY - CHAP A1 - Brost, Julian A1 - Egger, Christoph A1 - Lai, Russell A1 - Schmid, Fritz A1 - Schröder, Dominique A1 - Zoppelt, Markus T1 - Threshold Password-Hardened Encryption Services N2 - Password-hardened encryption (PHE) was introduced by Lai et al. at USENIX 2018 and immediately productized by VirgilSecurity. PHE is a password-based key derivation protocol that involves an oblivious external crypto service for key derivation. The security of PHE protects against offline brute-force attacks, even when the attacker is given the entire database. Furthermore, the crypto service neither learns the derived key nor the password. PHE supports key-rotation meaning that both the server and crypto service can update their keys without involving the user. While PHE significantly strengthens data security, it introduces a single point of failure because key-derivation always requires access to the crypto service. In this work, we address this issue and simultaneously increase security by introducing threshold password-hardened encryption. Our formalization of this primitive revealed shortcomings of the original PHE definition that we also address in this work. Following the spirit of prior works, we give a simple and efficient construction using lightweight tools only. We also implement our construction and evaluate its efficiency. Our experiments confirm the practical efficiency of our scheme and show that it is more efficient than common memory-hard functions, such as scrypt. From a practical perspective this means that threshold PHE can be used as an alternative to scrypt for password protection and key-derivation, offering better security in terms of offline brute force attacks. CCS CONCEPTS • Security and privacy → Cryptography. Y1 - 2020 U6 - https://doi.org/10.1145/3372297.3417266 SP - 409 EP - 424 PB - ACM ER - TY - CHAP A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - UnCle SAM: Modeling Cloud Attacks with the Automotive Security Abstraction Model T2 - CLOUD COMPUTING 2019 : The Tenth International Conference on Cloud Computing, GRIDs, and Virtualization N2 - Driverless (autonomous) vehicles will have greater attack potential than any other individual mobility vehicles ever before. Most intelligent vehicles require communication interfaces to the environment, direct connections (e.g., Vehicle-to-X (V2X)) to an Original Equipment Manufacturer (OEM) backend service or a cloud. By connecting to the Internet, which is not only necessary for the infotainment systems, cars could increasingly turn into targets for malware or botnet attacks. Remote control via the Internet by a remote attacker is also conceivable, as has already been impressively demonstrated. This paper examines security modeling for cloud-based remote attacks on autonomous vehicles using a Security Abstraction Model (SAM) for automotive software systems). SAM adds to the early phases of (automotive) software architecture development by explicitly documenting attacks and handling them with security techniques. SAM also provides the basis for comprehensive security analysis techniques, such as the already available Common Vulnerability Scoring System (CVSS) or any other attack assessment system. KW - Automotive Security KW - Automotive Software Engineering KW - Security Modeling KW - Cloud Attacks KW - OTA Updates Y1 - 2019 SN - 978-1-61208-703-0 ER - TY - CHAP A1 - Zoppelt, Markus A1 - Tavakoli Kolagari, Ramin T1 - What Today's Serious Cyber Attacks on Cars Tell Us BT - Consequences for Automotive Security and Dependability T2 - Model-Based Safety and Assessment : 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings N2 - Highly connected with the environment via various interfaces, cars have been the focus of malicious cyber attacks for years. These attacks are becoming an increasing burden for a society with growing vehicle autonomization: they are the sword of Damocles of future mobility. Therefore, research is particularly active in the area of vehicle IT security, and in part also in the area of dependability, in order to develop effective countermeasures and to maintain a minimum of one step ahead of hackers. This paper examines the known state-of-the-art security and dependability measures based on a detailed and systematic analysis of published cyber attacks on automotive software systems. The sobering result of the analysis of the cyber attacks with the model-based technique SAM (Security Abstraction Model) and a categorization of the examined attacks in relation to the known security and dependability measures is that most countermeasures against cyber attacks are hardly effective. They either are not applicable to the underlying problem or take effect too late; the intruder has already gained access to a substantial part of the vehicle when the countermeasures apply. The paper is thus contributing to an understanding of the gaps that exist today in the area of vehicle security and dependability and concludes concrete research challenges. KW - Automotive Security KW - Automotive System Architecture KW - Dependability KW - Model-Driven Engineering Methodologies Y1 - 2019 SN - 978-3-030-32872-6 U6 - https://doi.org/10.1007/978-3-030-32872-6_18 SP - 219 EP - 285 PB - Springer CY - Cham ER -