Emerging ultra-low-power tiny scale computing devices run on harvested energy, are intermittently powered, have limited computational capability, and perform sensing and actuation functions under the control of a dedicated firmware operating without the supervisory control of an operating system. Wirelessly updating or patching firmware of such devices is inevitable. We consider the challenging problem of simultaneous and secure firmware updates or patching for a typical class of such devices—Computational Radio Frequency Identification (CRFID) devices. We propose Wisecr, the first secure and simultaneous wireless code dissemination mechanism to multiple devices that prevents malicious code injection attacks and intellectual property (IP) theft, whilst enabling remote attestation of code installation. Importantly, Wisecr is engineered to comply with existing ISO compliant communication protocol standards employed by CRFID devices and systems. We comprehensively evaluate Wisecr's overhead, demonstrate its implementation over standards compliant protocols, analyze its security, implement an end-to-end realization with popular CRFID devices and open-source the complete software package on GitHub.

Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints is intrinsically highly reliable. We derive formal performance bounds to support practitioners to easily adopt our methods for applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. Together with extensive simulations, we have employed 119 chips from five different manufacturers for extensive experimental validations. Our results, including an end-to-end implementation demonstration with a low-cost wearable Bluetooth inertial sensor capable of on-demand and runtime key generation, show that key generators with failure rates less than $10^{-6}$ can be efficiently obtained with noise-tolerant fingerprints with a single fingerprint snapshot to support ease-of-enrollment.

Internet of Things devices are widely adopted by the general population. People today are more connected than ever before. The widespread use and low-cost driven construction of these devices in a competitive marketplace render Internet-connected devices an easier and attractive target for malicious actors. This paper demonstrates non-invasive physical attacks against IoT devices in two case studies in a tutorial style format. The study focuses on demonstrating the: i) exploitation of debug interfaces, often left open after manufacture; and ii) the exploitation of exposed memory buses. We illustrate a person could commit such attacks with entry-level knowledge, inexpensive equipment, and limited time (in 8 to 25 minutes).

Physical Unclonable Function (PUF) is a hardware security primitive with a desirable feature of low-cost. Based on the space of challenge-response pairs (CRPs), it has two categories:weak PUF and strong PUF. Though designing a reliable and secure lightweight strong PUF is challenging, there is continuing efforts to fulfill this gap due to wide range of applications enabled by strong PUF. It was prospected that the combination of MAX and MIN bit-wise operation is promising for improving the modeling resilience when MAX and MIN are employed in the PUF recomposition. The main rationale lies on the fact that each bit-wise might be mainly vulnerable to one specific type of modeling attack, combining them can have an improved holistic resilience. This work is to first evaluate the main PUF performance, in particular,uniformity and reliability of the OR-AND-XOR-PUF(OAX-PUF)-(x, y, z)-OAX-PUF. Compared with the most used l-XOR-PUF, the (x, y, z)-OAX-PUF eventually exhibits better reliability given l=x+y+z without degrading the uniformity retaining to be 50%. We further examine the modeling resilience of the (x, y, z)-OAX-PUF with four powerful attacking strategies to date, which are Logistic Regression (LR) attack, reliability assisted CMA-ES attack, multilayer perceptron (MLP) attack, and the most recent hybrid LR-reliability attack. In comparison with the XOR-APUF, the OAX-APUF successfully defeats the CAM-ES attack. However, it shows no notable modeling accuracy drop against other three attacks, though the attacking times have been greatly prolonged to LR and hybrid LR-reliability attacks. Overall, the OAX recomposition could be an alternative lightweight recomposition method compared to XOR towards constructing strong PUFs if the underlying PUF, e.g., FF-APUF, has exhibited improved resilience to modeling attack, because the OAX incurs smaller reliability degradation compared to XOR.

This paper presents a method for remotely and dynamically determining the execution schedule of long-running tasks on intermittently powered devices such as computational RFID. Our objective is to prevent brown-out events caused by sudden power-loss due to the intermittent nature of the powering channel. We formulate, validate and demonstrate that the read-rate measured from an RFID reader (number of successful interrogations per second) can provide an adequate means of estimating the powering channel condition for passively powered CRFID devices. This method is attractive because it can be implemented without imposing an added burden on the device or requiring additional hardware. We further propose ReaDmE, a dynamic execution scheduling scheme to mitigate brownout events to support long-run execution of complex tasks, such as cryptographic algorithms, on CRFID. Experimental results demonstrate that the ReaDmE method can improve CRFID's long-run execution success rate by 20% at the critical operational range or reduce time overhead by up to 23% compared to previous execution scheduling methods.

The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery-free, computational, sensing enhanced devices that harvest all of their energy for operation. Despite wireless connectivity and powering, secure wireless firmware updates remains an open challenge for CRFID devices due to: intermittent powering, limited computational capabilities, and the absence of a supervisory operating system. We present, for the first time, a secure wireless code dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic hardware security primitive Static Random Access Memory Physical Unclonable Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i) overcomes the resource-constrained and intermittently powered nature of the CRFID devices; ii) is fully compatible with existing communication protocols employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is built upon a standard and industry compliant firmware compilation and update method realized by extending a recent framework for firmware updates provided by Texas Instruments. We build an end-to-end SecuCode implementation and conduct extensive experiments to demonstrate standards compliance, evaluate performance and security.

A securely maintained key is the premise upon which data stored and transmitted by ubiquitously deployed resource limited devices, such as those in the Internet of Things (IoT), are protected. However, many of these devices lack a secure non-volatile memory (NVM) for storing keys because of cost constraints. Silicon physical unclonable functions (PUFs) offering unique device specific secrets to electronic commodities are a low-cost alternative to secure NVM. As a physical hardware security primitive, reliability of a PUF is affected by thermal noise and changes in environmental conditions; consequently, PUF responses cannot be directly employed as cryptographic keys. A fuzzy extractor can turn noisy PUF responses into usable cryptographic keys. However, a fuzzy extractor is not immediately mountable on (highly) resource constrained devices due to its implementation overhead. We present a methodology for constructing a lightweight and secure PUF key generator for resource limited devices. In particular, we focus on PUFs constructed from pervasively embedded SRAM in modern microcontroller units and use a batteryless computational radio frequency identification (CRFID) device as a representative resource constrained IoT device in a case study.

Recently, we have witnessed the emergence of intermittently powered computational devices, an early example is the Intel WISP (Wireless Identification and Sensing Platform). How we engineer basic security services to realize mutual authentication, confidentiality and preserve privacy of information collected, stored and transmitted by, and establish the veracity of measurements taken from, such devices remain an open challenge; especially for batteryless and intermittently powered devices. While the cryptographic community has significantly progressed lightweight (in terms of area overhead) security primitives for low cost and power efficient hardware implementations, lightweight software implementations of security primitives for resource constrained devices are less investigated. Especially, the problem of providing security for intermittently powered computational devices is unexplored. In this paper, we illustrate the unique challenges posed by an emerging class of intermittently powered and energy constrained computational IoT devices for engineering security solutions. We focus on the construction and evaluation of a basic hash primitive---both existing cryptographic hash functions and non-cryptographic hash functions built upon lightweight block ciphers. We provide software implementation benchmarks for eight primitives on a low power and resource limited computational device, and outline an execution model for these primitives under intermittent powering.

A physical unclonable function (PUF), like a fingerprint, exploits manufacturing randomness to endow each physical item with a unique identifier. One primary PUF application is the secure derivation of volatile cryptographic keys using a fuzzy extractor (FE) comprising: 1) a secure sketch and 2) an entropy extractor. Although the entropy extractor can be lightweight, the overhead of the secure sketch responsible for correcting naturally noisy PUF responses is usually high. We observe that, in general, response unreliability with respect to an enrolled reference measurement increases with increasing differences between the in-the-field PUF operating condition and the operating condition used in evaluating the enrolled reference response. For the first time, we exploit such an inadvertent but important observation. In contrast to the conventional single reference response enrollment, we propose enrolling multiple reference responses (MRRs) subject to the same challenge but under multiple distinct operating conditions. The critical observation here is that one of the reference operating conditions is likely to be closer to the operating condition of the field deployed PUF, thus resulting in minimizing the expected unreliability when compared to the single reference under the nominal condition. As a consequence, MRR greatly reduces the demand for the expected number of erroneous bits requiring correction and, subsequently, achieves a significant reduction in the error correction overhead. The significant implementation efficiency gains from the proposed MRR method are demonstrated from software implementations of FEs on batteryless resource constraint computational radio frequency identification devices, where realistic PUF data are collected from intrinsic static random access memory PUFs.

The Universal Serial Bus (USB) is the most prominent interface for connecting peripheral devices to computers. USB-connected input devices, such as keyboards, cardswipers and fingerprint readers, often send sensitive information to the computer. As such information is only sent along the communication path from the device to the computer, it was hitherto thought to be protected from potentially compromised devices outside this path. We have tested over 50 different computers and external hubs and found that over 90% of them suffer from a crosstalk leakage effect that allows malicious peripheral devices located off the communication path to capture and observe sensitive USB traffic. We also show that in many cases this crosstalk leakage can be observed on the USB power lines, thus defeating a common USB isolation countermeasure of using a charge-only USB cable which physically disconnects the USB data lines. Demonstrating the attack’s low costs and ease of concealment, we modify a novelty USB lamp to implement an off-path attack which captures and exfiltrates USB traffic when connected to a vulnerable internal or a external USB hub.

Population ageing is a global phenomena demanding novel technological developments as a result of increased healthcare costs and wishes of older people to stay in their own homes for longer. Combining the ubiquity of Internet connectivity in homes with pervasive and intelligent sensors to exploit user interactions with everyday home appliances create the possibility to build a new generation of assistive technologies for smart homes. We present a demonstration of HoTAAL (Home of Things for Ambient Assisted Living) where appliances exhibit seamless social interactions with each other and older people to provide assistance with daily living activities, especially in meal preparation tasks. These technologies are unobtrusive and able to coherently interconnect with older people in a naturalistic manner.

Measuring multiple physical quantities are increasingly being demanded in commercial, biomedical and generally in ubiquitous applications. Although the recent emergence of passive sensor enabled RFID tags (sensor tags) provide new opportunities for these types of applications mainly due to the extended operational life and the small form factor, the energy harvesting nature of sensor tags hinders the use of multiple sensors in a single platform because of the requirement of additional energy to operate multiple sensors and subsequent reduction in the throughput. In this paper, we propose three, fast and energy efficient multi-sensor data retrieval approaches to obtain sensor data from sensor tags. We implemented a sensor tag with two sensors, an accelerometer and a barometer. Our extensive experiments on power consumption, operational range and throughput using the developed sensor tag revealed that, the proposed approaches can successfully be used for multi-sensor data retrieval and indicates that they can effectively be used in a range of real-world ubiquitous sensing applications such as fall prevention and food safety monitoring.