The ubiquity and pervasiveness of modern Internet of Things (IoT) devices opens up vast possibilities for novel applications, but simultaneously also allows spying on, and collecting data from, unsuspecting users to a previously unseen extent. This paper details a new attack form in this vein, in which the decay properties of widespread, off-the-shelf DRAM modules are exploited to accurately spy on the temperature in the vicinity of the DRAM-carrying device. Among others, this enables adversaries to remotely and purely digitally spy on personal behavior in users’ private homes, or to collect security-critical data in server farms, cloud storage centers, or commercial production lines. We demonstrate that our attack can be performed by merely compromising the software of an IoT device and does not require hardware modifications or physical access at attack time. It can achieve temperature resolutions of up to 0.5°C over a range of 0°C to 70°C in practice. The presented attack works in devices that do not have a dedicated temperature sensor on board; as the DRAM modules already present in the device are abused to spy on the temperature. To complete the work, the paper discusses practical attack scenarios as well as possible countermeasures against the new temperature-spying attacks.

Low-end computing devices are becoming increasingly ubiquitous, especially due to the widespread deployment of Internet-of-Things products. There is, however, much concern about sensitive data being processed on these low-end devices which have limited protection mechanisms in place. This paper proposes a Hardware-Entangled Software Protection (HESP) scheme that leverages hardware features to protect software code from malicious modification before or during run-time. It also enables implicit hardware authentication. Thus, the software will execute correctly only on an authorized device and if the timing of the software, e.g., control flow, was not changed through malicious modifications. The proposed ideas are based on the new concept of Dynamic Physically Unclonable Functions (PUFs). Dynamic PUFs have time-varying responses and can be used to tie the software execution to the timing of software and the physical properties of a hardware device. It is further combined with existing approaches for code self-checksumming, software obfuscation, and call graph and register value scrambling to create the HESP scheme. HESP is demonstrated on commodity, off-the-shelf computing devices, where a DRAM PUF is used as an instance of a Dynamic PUF. The protection scheme can be applied automatically to LLVM Intermediate Representation (IR) code through an AutoPatcher written in Python. For a sample program containing AES encryption and decryption routine, HESP introduces 48% execution time overhead and increases the binary file size by 32.5%, which is moderate compared to other schemes such as software obfuscation. It takes about 2.6 seconds on average for the tested programs to be patched and compiled through the modified compilation flow and scripts.

Today's ubiquitous IoT devices make spying on, and collecting data from, unsuspecting users possible. This paper shows a new attack where DRAM modules, widely used in IoT devices, can be abused to measure the temperature in the vicinity of the device in order to spy on a user's behavior. Specifically, the temperature dependency of the DRAM decay is used as a proxy for user's behavior in the vicinity of the device. The attack can be performed remotely by only changing the software of an IoT device, without requiring hardware changes, and with a resolution reaching 0.5°C. Potential defenses to the temperature spying attack are presented in this paper as well.

Physical variations in the manufacturing processes of electronic devices have been widely leveraged to design Physically Unclonable Functions (PUFs), which can be used for authentication and key storage. Existing PUFs are static, as their PUF responses remain the same regardless when the PUF is queried. Meanwhile, this paper presents the new concept of Dynamic PUFs, where the responses depend not only on the physical properties of the device but also on the timing of the PUF queries. One application of Dynamic PUFs is in dynamic software-hardware binding, where the control flow of the software can be tied to both the timing of the software and the physical properties of the hardware, in order to protect software execution. This paper presents a realization of Dynamic PUFs using DRAM modules. The evaluation is based on the decay-based DRAM PUFs, which can be realized today and were implemented on commodity devices for testing.

A Physically Unclonable Function (PUF) is a unique and stable physical characteristic of a piece of hardware, which emerges due to variations in the hardware fabrication processes. Prior works have demonstrated that PUFs are a promising cryptographic primitive that can enable secure key storage, hardware-based device authentication and identification. So far, most PUF constructions have required an addition of new hardware or an FPGA implementation for their operation. Recently, intrinsic PUFs, which can be found in commodity devices, have been investigated. Unfortunately, most of them suffer from the drawback that they can only be accessed at boot time. This paper focuses on a new class of run-time accessible, decay-based, intrinsic DRAM PUFs in commercial off-the-shelf systems, which requires no additional hardware or FPGAs. In order to enable secure key storage using DRAM PUFs, this work presents a new Helper Data System (HDS) specifically tailored to the properties of the decay process inherent to DRAM cells. The decay-based DRAM PUF and the new HDS are evaluated on commodity off-the-shelf devices to demonstrate their practicality. Furthermore, a novel lightweight protocol is presented that allows for mutual authentication.

In this work, we present the first systematic study of data remanence effects on an intrinsic Static Random Access Memory Physical Unclonable Function (SRAM PUF) implemented on a commercial off-the-shelf (COTS) device in the temperature range between –110 degrees Celsius and –40 degrees Celsius. Based on our experimental results, we propose a new type of attack against intrinsic SRAM PUFs, which takes advantage of data remanence effects exhibited due to low temperatures. We demonstrate that this attack is highly resistant to memory erasure techniques and can be used to manipulate the cryptographic keys produced by the SRAM PUF. Finally, we also discuss and assess potential countermeasures against the attack we propose.

In this paper we examine the use of covert channels based on CPU load in order to achieve persistent user identification through browser sessions. In particular, we demonstrate that an HTML5 video, a GIF image, or CSS animations on a webpage can be used to force the CPU to produce a sequence of distinct load levels, even without JavaScript or any client-side code.

In recent years, physically unclonable functions (PUFs) have been proposed as a promising building block for key storage and device authentication. PUFs are physical systems, and as such, their responses are inherently noisy, precluding a straightforward derivation of cryptographic key material from raw PUF measurements. To overcome this drawback, fuzzy extractors are used to eliminate the noise and guarantee robust outputs. A special type is reverse fuzzy extractors, shifting the computational load of error correction toward a computationally powerful verifier. However, the reverse fuzzy extractor reveals error patterns to any eavesdropper, which may cause privacy issues (due to a systematic drift of the PUF responses, the error pattern is linkable to the identity) and even security problems (if the noise is data-dependent). In this paper, we quantify the issue of leakage due to asymmetry of noise, leveraging the binary asymmetric channel (BAC) model. We further propose to concatenate two BACs to form a symmetric channel, as a solution that is able to eliminate such noise. Finally, we propose a modified reverse fuzzy extractor that does not leak via the error patterns even in the case of systematic drift of the PUF responses.

Research on Physically Unclonable Functions (PUFs) has become very popular in recent years. However, all PUFs researched so far require either ASICs, FPGAs or a microcontroller with external components. Our research focuses on identifying PUFs in commercial off-the-shelf devices, e.g. microcontrollers. We show that PUFs exist in several off-theshelf products, which can be used for security applications. We present measurement results on the PUF behavior of five of the most popular microcontrollers today: ARM Cortex A,ARM Cortex-M,Atmel AVR, Microchip PIC16 and Texas Instruments MSP430. Based on these measurements, we can calculate whether these chips can be considered for applications requiring strong cryptography. As a result of these findings, we present a secure bootloader for the ARM Cortex-A9 platform based on a PUF inherent to the device, requiring no external components. Furthermore, instead of discarding the randomness in PUF responses, we utilize this to create strong seeds for pseudo-random number generators (PRNGs). The existence of a secure RNG is at the heart of virtually every cryptographic protocol, yet very often overlooked. We present the implementation of a strongly seeded PRNG for the ARM Cortex-M family, again requiring no external components.