Executive Summary This report summarizes our study of anomaly detection techniques using readings from the AC power cord on an infusion pump. Our goal was to measure the effectiveness of our power analysis techniques in identifying anomalous pump behavior that could include intentional tampering via cyber-physical attacks. Our system was able to detect simulated cyberattack scenarios with close to 100% accuracy. The tunable model is configurable to generate near-zero false positives.

We present the first power over Wi-Fi system that delivers power to low-power sensors and devices and works with existing Wi-Fi chipsets. We show that a ubiquitous part of wireless communication infrastructure, the Wi-Fi router, can provide far field wireless power without significantly compromising the network's communication performance. Building on our design, we prototype battery-free temperature and camera sensors that we power with Wi-Fi at ranges of 20 and 17 ft, respectively. We also demonstrate the ability to wirelessly trickle-charge nickel--metal hydride and lithium-ion coin-cell batteries at distances of up to 28 ft. We deploy our system in six homes in a metropolitan area and show that it can successfully deliver power via Wi-Fi under real-world network conditions without significantly degrading network performance.

Medical devices increasingly depend on software. While this expands the ability of devices to perform key therapeutic and diagnostic functions, reliance on software inevitably causes exposure to hazards of security vulnerabilities. This article uses a recent high-profile case example to outline a proactive approach to security awareness that incorporates a scientific, risk-based analysis of security concerns that supports ongoing discussions with patients about their medical devices.

Software-based medical devices enable fast product-development cycles, constructive information sharing, and configurable therapy delivery, resulting in better patient outcomes overall. An unfortunate drawback is that software is complicated and difficult to maintain correctly. Devices with inadequate software maintenance may pose operational risks to network security and patient safety and privacy. This paper describes a noninvasive approach to medical-device monitoring that can address some of the shortcomings of conventional approaches.Protecting software-based medical devices from malware infections or network-based mischief is a growing concern for clinical engineers and healthcare information technology (IT) practitioners. Unlike desktop PCs and laptops, medical devices often lack support for antivirus systems or operating-system patches, despite running off-the-shelf operating systems and commercial third-party software. Manufacturers have cited previous regulatory approval as a reason not to support software updates [1], despite the Food and Drug Administration's clarifications to the contrary [2].Medical devices are often in use for decades in clinical settings, during which time the software they are based on continues to change. For example, Microsoft Windows has undergone four major product revisions since the release of Windows XP in 2001, but new medical devices are shipped with Windows XP as recently as 2012 [3], and anecdotal evidence suggests many more are still in use [4]. Microsoft halted support for Windows XP in early 2014. Even when patches are available, administrators tend to emphasize functionality and efficacy over security and avoid applying patches [4] for fear of breaking systems or voiding warranties.Without adequate patching, the threat to connected devices increases with time as more vulnerabilities are discovered. Manufacturers have little incentive to retest devices once they are in the field [5], and testing is not guaranteed to catch vulnerabilities. While proactive manufacturers have been steadily improving their design and maintenance processes for new devices to prevent security holes and permit software patches, healthcare IT practitioners are often left with a mess they cannot effectively maintain.Third-party software also poses challenges for manufacturers and device owners. Devices often ship with commercial or open-source libraries that are maintained separately from a device's main code, often by completely separate teams. Popular libraries that are easy to use become widespread if they add new capabilities to devices. For example, the OpenSSL library for encrypting communications, which is compatible with a range of systems from embedded to server-class, appears in at least 74 different kinds of devices, each with different update mechanisms. A 2014 Internet scan for a particularly high-impact vulnerability called Heartbleed found that 56% of vulnerable devices were embedded systems [6], which are typically more difficult to update than PCs or servers. Modern applications bring together tens of libraries that must all be considered separate sources of potential security problems.The National Institute of Standards and Technology (NIST) recently published a medical-device security use case [5] describing the security risks of a modern medical device, an infusion pump with wireless network connectivity. Network connectivity gives this device a means of adjusting therapy, feeding data into electronic health records (EHRs), and working with centralized management tools. NIST also pointed out the risk of bad actors changing infusion rates to harm patients, a potentially dangerous criminal offense. Independent researchers have confirmed that commercially available devices are vulnerable to such tampering [7]. Malicious hackers may be tempted by a potential foothold into a hospital network [8], a desire to harm specific patients or other mischievous ends [9].The remainder of this brief summarizes an approach to medical-device monitoring that can be added to medical devices in postmarket scenarios, i.e., after they are deployed in clinical settings. Monitoring is a component of security, along with prevention and remediation. In light of the NIST use case, the example in this brief focuses on monitoring infusion pumps for unauthorized dosage changes.Our system, which draws inspiration from previously published work [10,11], comprises hardware and software dedicated to nonintrusive monitoring. We use machine learning to match a device's activity patterns to previously observed behaviors. The monitoring point for device behaviors is the AC power outlet, a common interface to many medical devices. For many kinds of plugged-in devices, distinct activities on the device correspond to distinct patterns of power consumption. In the case of an infusion pump, the relevant question is whether different infusion rates can be distinguished by examining power consumption.We studied three commercially available infusion pumps from two manufacturers: two large-volume pumps and a low-volume syringe-type pump. Each runs an off-the-shelf operating system. Our system flags anomalous behavior by comparing behavioral samples against a model derived from a training set. For each pump, we first established a set of training data by recording only the pump's normal behavior with our prototype hardware. We used a proprietary feature-engineering workflow to determine which properties of the collected signals revealed the most about the system's aggregate behavior. With a model derived from the training data, we commenced “live” measurement of the pump to compare behavioral samples to the trained model using the selected features. If a series of new measurements were sufficiently outside the normal range with respect to a sufficient number of features, our detectors automatically deemed the pump's behavior as anomalous.We qualified “normal” behavior on the two large-volume pumps as an infusion rate of 50 ml/hr, and on the syringe-type pump as an infusion rate of 0.1 ml/hr. Normal depends on the drug being delivered, the patient, and other clinical decision processes, but in keeping with the NIST threat model, we focused on scenarios in which an adversary would be able to change a low infusion rate to an inappropriately high infusion rate. We qualified an “abnormal” rate on the large-volume pumps as 999 ml/hr, the maximum configurable, and on the syringe-type pump as its maximum 20 ml/hr for a simulated morphine sulfate 5 ml/hr syringe.Over all the infusion pumps we tested, our tools automatically generated models that discriminated among the various infusion rates with low rates of false positives (1.0% and lower) and false negatives (close to 0.0%).Figure 1 is a visualization of an infusion pump anomaly detection model (a one-class support vector machine with two features) that our prototype toolchain produced. The X- and Y-axes plot the two features used by this model of a high-volume infusion pump. Each dot represents one measurement of the pump's power consumption over 100 s, with respect to two features we used to build the model. Normal data collected from the pump at a 50 ml/hr delivery rate (white dots) were used as training data. Next, we evaluated the model with new measurements of normal 50 ml/hr infusion rates (green dots), which fall inside the red boundary, indicating no anomaly. Finally, we evaluated the model on new measurements that simulated abnormal (attack) activity at a 999 ml/hr rate. These dots fall outside the red boundary, indicating an anomaly. Our error rate on the training data was 1.0%, with 0.0% error for normal test data (false positives) and 0.0% error on abnormal test data (false negatives). The trade-off between false negatives and false positives is configurable in the model. For a second high-volume pump with a similar training and testing regimen, the error on our training data was 0.1% (false positives), and error on the test data was 0.0% (false negatives).In addition to anomaly detection, we also built a regression model to predict the infusion rate using only features measured from the AC power line; the plot is omitted for space. The input to this model is the X-axis feature from Fig. 1, measured over 100 s. The output is the predicted infusion rate, which is a continuous value in contrast to the discrete normal versus abnormal output of the anomaly detector. The model suggests that infusion rate is correlated (R2 = 0.82) to the features we measured on the pump's AC power traces.Our preliminary study demonstrates that commercially available infusion pumps' patterns of AC power consumption are correlated with their infusion rates, suggesting that monitoring power at fine granularity is a potentially viable approach to postmarket security maintenance for these medical devices. Postmarket strategies cannot replace coherent patching and update strategies by manufacturers, but they can help decrease healthcare IT practitioners' reliance on manufacturers to provide updates in lockstep with known vulnerabilities in off-the-shelf components.This material is based upon work supported by the National Science Foundation under the SBIR Grant No. 1448440.

Integrity checking is ubiquitous in data networks, but not all network traffic needs integrity protection. Many applications can tolerate slightly damaged data while still working acceptably, trading accuracy versus efficiency to save time and energy. Such applications should be able to receive damaged data if they so desire. In today's network stacks, lower-layer integrity checks discard damaged data regardless of the application's wishes, violating the End-to-End Principle. This paper argues for optional integrity checking and gently redesigns a commodity network architecture to support integrity-unprotected data. Our scheme, called Selective Approximate Protocol (SAP), allows applications to coordinate multiple network layers to accept potentially damaged data. Unlike previous schemes that targeted video or media streaming, SAP is generic. SAP's improved throughput and decreased retransmission rate is a good match for applications in the domain of approximate computing. Implemented atop WiFi as a case study, SAP works with existing physical layers and requires no hardware changes. SAP's benefits increase as channel conditions degrade. In tests of an error-tolerant file-transfer application over WiFi, SAP sped up transmission by about 30% on average.

Energy harvesting enables novel devices and applications without batteries, but intermittent operation under energy harvesting poses new challenges to memory consistency that threaten to leave applications in failed states not reachable in continuous execution. This paper presents analytical models that aid in reasoning about intermittence. Using these, we develop DINO (Death Is Not an Option), a programming and execution model that simplifies programming for intermittent systems and ensures volatile and nonvolatile data consistency despite near-constant interruptions. DINO is the first system to address these consistency problems in the context of intermittent execution. We evaluate DINO on three energy-harvesting hardware platforms running different applications. The applications fail and exhibit error without DINO, but run correctly with DINO’s modest 1.8–2.7× run-time overhead. DINO also dramatically simplifies programming, reducing the set of possible failure- related control transfers by 5–9×.

Energy-scavenging devices with general-purpose microcontrollers can support arbitrarily complex sensing tasks in theory, but in practice, energy limitations impose severe constraints on the application space. Richer sensing such as image capture would enable many new applications to take advantage of energy scavenging. Richer sensing faces two key challenges: efficiently retaining the necessary amount of harvested energy, and storing and communicating large units of sensor data. This paper presents the WISPCam, a passive UHF RFID camera tag based on the Wireless Identification and Sensing Platform that overcomes these two challenges to support reliable image capture and transmission while powered by an RFID reader. The WISPCam uses a novel charge-storage scheme designed specifically to match the image sensor's needs. This scheme optimally balances capacitance and leakage to improve the sensitivity and efficiency of the power harvester. The WISPCam also uses a novel data storage and communication scheme to reliably support the transfer of complete images to an RFID reader application. The WISPCam makes battery-free image capture practical for applications such as mechanical gauge reading and surveillance, both demonstrated in this paper, and opens the door to richer sensing applications on battery-free devices.

Approximate computing trades off accuracy for better performance and energy efficiency. It offers promising optimization opportunities for a wide variety of modern applications, from mobile vision to data analytics. Recent approaches to approximate computing have relied on either manual program modification, based exclusively on programmer reasoning, or opaque automatic transformations, which sacrifice programmer control. We describe ACCEPT, a comprehensive framework for approximation that balances automation with programmer guidance. It includes C/C++ type qualifiers for constraining approximation, a compiler analysis library that identifies regions of approximable code, an autotuning system that automatically chooses the best approximation strategies, and a feedback mechanism that explains how annotations can be improved for better approximation opportunities. ACCEPT automatically applies a variety of approximation techniques, including hardware acceleration, while ensuring their safety. We apply ACCEPT to nine workloads on a standard desktop, an FPGA-augmented mobile SoC, and an energy-harvesting sensor device to evaluate the annotation process. We observe average speedups of 2.3×, 4.8×, and 1.5× on the three platforms, respectively.

Approximate computing exposes opportunities to increase the efficiency of computer systems by trading-off accuracy for energy savings. These optimization opportunities can be found in many emerging classes of applications which exhibit a degree of tolerance to imprecision. Taking full advantage of approximation requires a programming model that provides control to the programmer over what portions of a program can be approximated, as well as hardware-based techniques that can effectively trade-off accuracy for energy savings. We describe ACCEPT, a compiler framework for approximate programs and SNNAP, an approximate accelerator that can efficiently offload regions of approximate code. Using ACCEPT, a software programmer can leverage the benefits of approximate computing by annotating software with data-centric annotations and letting the compiler identify the best approximation strategies based on accuracy requirements. We evaluate programs that offload approximate computation on SNNAP, and observe an average speedup of 3.8× and an average energy saving of 2.8×.

Approximate computing trades off accuracy for better performance and energy efficiency. It offers promising optimization opportunities for a wide variety of modern applications, from mobile vision to data analytics. Recent approaches to approximate computing have relied on either manual program modification, based exclusively on programmer reasoning, or opaque automatic transformations, which sacrifice programmer control. We describe ACCEPT, a comprehensive framework for approximation that balances automation with programmer guidance. It includes C/C++ type qualifiers for constraining approximation, a compiler analysis library that identifies regions of approximable code, an autotuning system that automatically chooses the best approximation strategies, and a feedback mechanism that explains how annotations can be improved for better approximation opportunities. ACCEPT automatically applies a variety of approximation techniques, including hardware acceleration, while ensuring their safety. We apply ACCEPT to nine workloads on a standard desktop, an FPGA-augmented mobile SoC, and an energy-harvesting sensor device to evaluate the annotation process. We observe average speedups of 2.3×, 4.8×, and 1.5× on the three platforms, respectively.

Low-cost, easily deployable, reconfigurable, movable input devices can enable adaptive workflows in commercial, industrial, and home environments. A key limitation of previous reconfigurable control systems is their high cost or maintenance burden (e.g., battery changes or wiring setup). Our poster presents Arfid, a "fabric" for reconfigurable input devices that connects low-cost, battery-free inputs to arbitrarily specified functions in their surroundings via a buildingwide network of RFID readers. Users can reassign controllers' functions using a simple web interface.

Energy harvesting enables intermittently powered devices to compute without built-in power. But frequent power failures, combined with nonvolatile memory intended to protect computational state, introduce strange control flow that turns sequential code into unwieldy concurrent code: programs must grapple with their own state from previous interrupted runs. This paper describes the broken time machine problem for these devices and outlines potential solutions from the perspective of safe concurrent programming.

Approximation saves energy in computation and storage by allowing data to diverge from precise representations. In contrast, networks guarantee that transmissions are received precisely by enforcing integrity checks at multiple layers of the protocol stack; these checks result in onerous retransmissions and increased network contention especially in wireless networks. When energy is limited, such as on embedded systems, conservative network semantics can needlessly consume an application’s energy budget. Why should approximate data be transmitted precisely?

Medical devices based on embedded systems are ubiquitous in clinical settings. Increasingly, they connect to networks and run off-the-shelf operating systems vulnerable to malware. But strict validation requirements make it prohibitively difficult or costly to use anti-virus software or automated operating system updates on these systems. Our add-on monitoring system, WattsUpDoc, uses a traditionally undesirable side channel of power consumption to enable run-time malware detection. In our experiments, WattsUpDoc detected previously known malware with at least 94% accuracy and previously unknown malware with at least 85% accuracy on several embedded devices--detection rates similar to those of conventional malware-detection systems on PCs. WattsUpDoc detects malware without requiring hardware or software modifications or network communication.

Computers plugged into power outlets leak identifiable information by drawing variable amounts of power when performing different tasks. This work examines the extent to which this side channel leaks private information about web browsing to an observer taking measurements at the power outlet. Using direct measurements of AC power consumption with an instrumented outlet, we construct a classifier that correctly identifies unlabeled power traces of webpage activity from a set of 51 candidates with 99

Transiently powered computing devices such as RFID tags, kinetic energy harvesters, and smart cards typically rely on programs that complete a task under tight time constraints before energy starvation leads to complete loss of volatile memory. Mementos is a software system that transforms general-purpose programs into interruptible computations that are protected from frequent power losses by automatic, energy-aware state checkpointing. Mementos comprises a collection of optimization passes for the LLVM compiler infrastructure and a linkable library that exercises hardware support for energy measurement while managing state checkpoints stored in nonvolatile memory. We evaluate Mementos against diverse test cases in a trace-driven simulator of transiently powered RFID-scale devices. Although Mementos's energy checks increase run time when energy is plentiful, they allow Mementos to safely suspend execution when energy dwindles, effectively spreading computation across zero or more power failures. This paper's contributions are: a study of the runtime environment for programs on RFID-scale devices; an energy-aware state checkpointing system for these devices that is implemented for the MSP430 family of microcontrollers; and a trace-driven simulator of transiently powered RFID-scale devices.

Implantable medical devices, or IMDs, are increasingly being used to improve patients' medical outcomes. Designers of IMDs already balance safety, reliability, complexity, power consumption, and cost. However, recent research has demonstrated that designers should also consider security and data privacy to protect patients from acts of theft or malice, especially as medical technology becomes increasingly connected to other systems via wireless communications or the Internet. This survey paper summarizes recent work on IMD security. It discusses sound security principles to follow and common security pitfalls to avoid. As trends in power efficiency, sensing, wireless systems and bio-interfaces make possible new and improved IMDs, they also underscore the importance of understanding and addressing security and privacy concerns in an increasingly connected world.

Background Medical devices increasingly depend on computing functions such as wireless communication and Internet connectivity for software-based control of therapies and network-based transmission of patients’ stored medical information. These computing capabilities introduce security and privacy risks, yet little is known about the prevalence of such risks within the clinical setting. Methods We used three comprehensive, publicly available databases maintained by the Food and Drug Administration (FDA) to evaluate recalls and adverse events related to security and privacy risks of medical devices. Results Review of weekly enforcement reports identified 1,845 recalls; 605 (32.8%) of these included computers, 35 (1.9%) stored patient data, and 31 (1.7%) were capable of wireless communication. Searches of databases specific to recalls and adverse events identified only one event with a specific connection to security or privacy. Software-related recalls were relatively common, and most (81.8%) mentioned the possibility of upgrades, though only half of these provided specific instructions for the update mechanism. Conclusions Our review of recalls and adverse events from federal government databases reveals sharp inconsistencies with databases at individual providers with respect to security and privacy risks. Recalls related to software may increase security risks because of unprotected update and correction mechanisms. To detect signals of security and privacy problems that adversely affect public health, federal postmarket surveillance strategies should rethink how to effectively and efficiently collect data on security and privacy problems in devices that increasingly depend on computing systems susceptible to malware.