Bodies.dissemination-committee March 2022

bodies.dissemination-committee@internal.sparta.eu

2 participants
2 discussions

[SPARTA - bodies.dissemination-committee] WP6.2 Submission (CINI, Italy)

by Gianluca Roascio

Dear All, As CINI (Italy) WP6.2 members, we have been accepted with a paper at 27th IEEE European Test Symposium (ETS22). Title, authors and abstract of the paper are below. We would like to have your consent to acknowledge SPARTA in the published version of the paper. Title: Real-Time Control-Flow Integrity for Multicore Mixed-Criticality IoT Systems Authors: Vahid EFTEKHARI MOGHADAM, Paolo PRINETTO, Gianluca ROASCIO Abstract: The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safety-critical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both high-criticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS). Much of the control code for these devices is written in memory-unsafe languages such as C and C++. This makes them susceptible to powerful binary attacks, such as the famous Return-Oriented Programming (ROP). Control-Flow Integrity (CFI) is the most investigated security technique to protect against such threats. At now, CFI solutions for real-time embedded systems are not as mature as the ones for general-purpose systems, and even more, there is a lack of in-depth studies on how different operating systems with different security requirements and timing constraints can coexist on a single multicore platform. This paper aims at drawing attention to the subject, discussing the current scientific proposal, and in turn proposing a solution for an optimized asymmetric verification system for execution integrity. By using an embedded hypervisor, predefined cores could be dedicated to only high or low-criticality tasks, with the high-priority core being monitored by the lower-criticality core, relying on offline binary instrumentation and a light exchange of information and signals at runtime. The work also presents preliminary results about a possible implementation for multicore ARM platforms, running both RTOS and GPOS, both in terms of security and performance penalties. All the best, Gianluca Roascio -- *Gianluca ROASCIO* *CINI* - Laboratorio Nazionale Cybersecurity Sede di Torino c/o LINKS - Leading Innovation & Knowledge for Society Via Pier Carlo Boggio 61, I-10138 Torino TO - Italy Tel: +39 334 3762427 gianluca.roascio(a)consorzio-cini.it Skype: gianluca.roascio www.cybersecnatlab.it <http://www.consorzio-cini.it/>

3 years, 6 months

[SPARTA - bodies.dissemination-committee] Submission

by Xabier Etxeberria Barrio

Hello, We have submitted a paper on “Optimized Parameter Search Approach For Weight Modification Attack Targeting Deep Learning Models” to Applied Sciences journal. If it is accepted we will acknowledge the SPARTA project. Please see its abstract below. Best wishes, Xabi Title: Optimized Parameter Search Approach For Weight Modification Attack Targeting Deep Learning Models Authors: Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Raul Orduna-Urrutia, Iñigo Mendialdua Abstract. Deep Neural Network models have been developed in different fields bringing many advances in several tasks. However, they have also started to be incorporated into tasks with critical risk. That worries researchers who have been interested in studying possible attacks on these models, discovering a long list of threats from which every model should be defended. The weights modification attack is presented and discussed among researchers who have presented several versions and analyses about such a threat. It focuses on detecting the vulnerable weight to modify them, misclassifying the desired input data. Therefore, analyzing the different approaches of this attack can help to understand more precisely how to defend such vulnerabilities. In this work, a new version of the weight modification attack is presented. That approach is based on three processes: input data clusterization, weight selection, and the modification of the weights. The data clusterization allows attacking the model more precisely. The weight selection uses the gradient given by the input data to know the desired parameters. The modification is incorporated little by little via reduced noise. -- <https://www.vicomtech.org/> Xabier Etxeberria Barrio Researcher | Investigador xetxeberria(a)vicomtech.org +[34] 943 30 92 30 Digital Security | Seguridad digital <https://www.linkedin.com/company/vicomtech> <https://www.youtube.com/user/VICOMTech> <https://twitter.com/@Vicomtech> member of: <https://graphicsvision.ai/> La información que contiene este mensaje y sus adjuntos son confidenciales y están dirigidos exclusivamente a sus destinatarios. Si recibe este mensaje por error, se ruega nos lo comunique y proceda a su borrado. The information contained in this electronic message is intended only for the personal and confidential use of the recipients designated in the original message. If you have received this communication in error, please notify us immediately by replying to the message and deleting it from your computer.

3 years, 6 months

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Bodies.dissemination-committee March 2022