Daniel Dorfmeister

Daniel Dorfmeister, Flavio Ferrarotti, Bernhard Fischer et al.

More and more companies' Intellectual Property (IP) is being integrated into Neural Network (NN) models. This IP has considerable value for companies and, therefore, requires adequate protection. For example, an attacker might replicate a production machines' hardware and subsequently simply copy associated software and NN models onto the cloned hardware. To make copying NN models onto cloned hardware infeasible, we present an approach to bind NN models - and thus also the IP contained within them - to their underlying hardware. For this purpose, we link an NN model's weights, which are crucial for its operation, to unique and unclonable hardware properties by leveraging Physically Unclonable Functions (PUFs). By doing so, sufficient accuracy can only be achieved using the target hardware to restore the original weights, rendering proper execution of the NN model on cloned hardware impossible. We demonstrate that our approach accomplishes the desired degradation of accuracy on various NN models and outline possible future improvements.

Manuel Wiesinger, Daniel Dorfmeister, Stefan Brunthaler

Vulnerabilities emanating from DRAM errors pose a vexing problem that remains, as of yet, unsolved and elusive but cannot be ignored. Prior defenses focused on specific details of early RowHammer attacks and fail to generalize with the generalizations of recent RowHammer attacks. Even worse, it is presently not clear that techniques from prior defenses will be able to cope with these generalizations or if an entirely new approach is required. Although still work-in-progress, we have identified a new approach that combines memory allocation with principles underlying software diversity and shows promising early results. At first glance, software diversity seems to be an unlikely contender, since it faces seemingly insurmountable obstacles, primarily the lack of sufficient entropy in memory subsystems. Our system - called MAD, short for memory allocation diversity - leverages two novel, complementary spatial diversification techniques to overcome this entropy obstacle. Entropy aside, MAD offers ease-of-implementation, negligible performance impact, and is both hardware and software agnostic. From a security perspective, MAD's goal is to deter RowHammer attacks by delaying them to the maximum extent possible. Such a delay opens the door for a variety of additional responses, e.g., proactive rebooting, or complementary in-depth analysis of ongoing attacks that would be too slow for an always-on defense.

Martina Zeinzinger, Josef Langer, Florian Eibensteiner et al.

An SRAM Physical Unclonable Function (PUF) can distinguish SRAM modules by analyzing the inherent randomness of their start-up behavior. However, the effectiveness of this technique varies depending on the design and fabrication of the SRAM module. This study compares two similar microcontrollers, both equipped with on-chip SRAM, to determine which device produces a better SRAM PUF. Both microcontrollers are programmed with an identical SRAM PUF authentication routine and tested under varying ambient temperatures (ranging from 10 °C to 50 °C) to evaluate the impact of temperature on SRAM PUF performance. One embedded SRAM works significantly better than the other, even though the two models are closely related. The presented results can be used early in the design process to compare arbitrary on-chip SRAM models and see which is best suited for implementing an SRAM PUF.

Bernhard Fischer, Daniel Dorfmeister, Flavio Ferrarotti et al.

Embedded software used in industrial systems frequently relies on data that ensures the correct and efficient operation of these systems. Thus, companies invest considerable resources in fine-tuning this data, making it their valuable intellectual property (IP). We present a novel protection mechanism for this IP that combines hardware fingerprints with Boolean logic. Unlike usual copy-protection approaches, unauthorised copies of the software still run on cloned devices but suboptimally. According to our security evaluation, only a complex dynamic analysis of the protected software running on the genuine target device can reveal the secret data. This makes the protection offered by our method more difficult to bypass. Notably, our approach does not require additional hardware, relying only on relatively simple updates to the software. We evaluate our protection mechanism by binding the parameters of a PID controller to a microcontroller unit (MCU) by using a physically unclonable function (PUF) based on its SRAM.

Daniel Dorfmeister, Flavio Ferrarotti, Bernhard Fischer et al.

We introduce a novel copy-protection method for industrial control software. With our method, a program executes correctly only on its target hardware and behaves differently on other machines. The hardware-software binding is based on Physically Unclonable Functions (PUFs). We use symbolic execution to guarantee the preservation of safety properties if the software is executed on a different machine, or if there is a problem with the PUF response. Moreover, we show that the protection method is also secure against reverse engineering.