Christian Niesler (University of Duisburg-Essen), Sebastian Surminski (University of Duisburg-Essen), Lucas Davi (University of Duisburg-Essen)

Memory corruption attacks are a pre-dominant attack vector against IoT devices. Simply updating vulnerable IoT software is not always possible due to unacceptable downtime and a required reboot. These side-effects must be avoided for highly-available embedded systems such as medical devices and, generally speaking, for any embedded system with real-time constraints.
To avoid downtime and reboot of a system, previous research has introduced the concept of hotpatching. However, the existing approaches cannot be applied to resource-constrained IoT devices. Furthermore, possible hardware-related issues have not been addressed, i.e., the inability to directly modify the firmware image due to read-only memory.

In this paper, we present the design and implementation of HERA (Hotpatching of Embedded Real-time Applications) which utilizes hardware-based built-in features of commodity Cortex-M microcontrollers to perform hotpatching of embedded systems. HERA preserves hard real-time constraints while keeping the additional resource usage to a minimum. In a case study, we apply HERA to two vulnerable medical devices. Furthermore, we leverage HERA to patch an existing vulnerability in the FreeRTOS operating system. These applications demonstrate the high practicality and efficiency of our approach.

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Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

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Low-risk Privacy-preserving Electric Vehicle Charging with Payments

Andreas Unterweger, Fabian Knirsch, Clemens Brunner and Dominik Engel (Center for Secure Energy Informatics, Salzburg University of Applied Sciences, Puch bei Hallein, Austria)

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Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Xiaoyu Cao (Duke University), Minghong Fang (The Ohio State University), Jia Liu (The Ohio State University), Neil Zhenqiang Gong (Duke University)

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