Software defined batteries

Badam, Anirudh; Chandra, Ranveer; Dutra, J.A.; Ferrese, Anthony; Hodges, Steve; Hu, Pan; Meinershagen, Julia; Moscibroda, Thomas; Priyantha, Bodhi; Skiani, Evangelia D.

doi:10.1145/2815400.2815429

Cited by 40 publications

(14 citation statements)

References 22 publications

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“…Embedding modest computing capabilities in a battery is already feasible (see Figure 2). Moreover, software-defined batteries [1] leverage more advanced microcontrollers to squeeze efficiency from a heterogeneous set of batteries.…”

Section: Fig 2 Microcontroller Inside the Battery Of Samsung Galaxy S4mentioning

confidence: 99%

Power to peep-all: Inference Attacks by Malicious Batteries on Mobile Devices

Lifshits

Forte

Hoshen

et al. 2018

Proceedings on Privacy Enhancing Technologies

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Mobile devices are equipped with increasingly smart batteries designed to provide responsiveness and extended lifetime. However, such smart batteries may present a threat to users’ privacy. We demonstrate that the phone’s power trace sampled from the battery at 1KHz holds enough information to recover a variety of sensitive information. We show techniques to infer characters typed on a touchscreen; to accurately recover browsing history in an open-world setup; and to reliably detect incoming calls, and the photo shots including their lighting conditions. Combined with a novel exfiltration technique that establishes a covert channel from the battery to a remote server via a web browser, these attacks turn the malicious battery into a stealthy surveillance device. We deconstruct the attack by analyzing its robustness to sampling rate and execution conditions. To find mitigations we identify the sources of the information leakage exploited by the attack. We discover that the GPU or DRAM power traces alone are sufficient to distinguish between different websites. However, the CPU and power-hungry peripherals such as a touchscreen are the primary sources of fine-grain information leakage. We consider and evaluate possible mitigation mechanisms, highlighting the challenges to defend against the attacks. In summary, our work shows the feasibility of the malicious battery and motivates further research into system and application-level defenses to fully mitigate this emerging threat.

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Section: Fig 2 Microcontroller Inside the Battery Of Samsung Galaxy S4mentioning

confidence: 99%

Power to peep-all: Inference Attacks by Malicious Batteries on Mobile Devices

Lifshits

Forte

Hoshen

et al. 2018

Proceedings on Privacy Enhancing Technologies

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“…To account for this effect, another metric that is useful is peak power draw of the radio. Higher peak power draw implies worse lifetime from batteries, since battery decay curves are linked to not just the average power draw but also the peak power draw [16] [15] [7]. It also means that tags would need more complex batteries with built-in power management circuits to be able to sustain the burst during active mode.…”

Section: Fs-backscatter Vs Ble/zigbeementioning

confidence: 99%

Enabling Practical Backscatter Communication for On-body Sensors

Zhang

Rostami

et al. 2016

Proceedings of the 2016 ACM SIGCOMM Conference

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252

127

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In this paper, we look at making backscatter practical for ultra-low power on-body sensors by leveraging radios on existing smartphones and wearables (e.g. WiFi and Bluetooth). The difficulty lies in the fact that in order to extract the weak backscattered signal, the system needs to deal with self interference from the wireless carrier (WiFi or Bluetooth) without relying on built-in capability to cancel or reject the carrier interference.Frequency-shifted backscatter (or FS-Backscatter) is based on a novel idea -the backscatter tag shifts the carrier signal to an adjacent non-overlapping frequency band (i.e. adjacent WiFi or Bluetooth band) and isolates the spectrum of the backscattered signal from the spectrum of the primary signal to enable more robust decoding. We show that this enables communication of up to 4.8 meters using commercial WiFi and Bluetooth radios as the carrier generator and receiver. We also show that we can support a range of bitrates using packet-level and bit-level decoding methods. We build on this idea and show that we can also leverage multiple radios typically present on mobile and wearable devices to construct multi-carrier or multi-receiver scenarios to improve robustness. Finally, we also address the problem of designing an ultra-low power tag that can frequency shift by 20MHz while consuming tens of micro-watts. Our results show that FS-Backscatter is practical in typical mobile and static on-body sensing scenarios while only using commodity radios and antennas.

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“…As a result, this attack can be either categorized as an availability or denial-of-service (DoS) attack, with potential consequences of causing damage or severely injuring humans. It goes without saying, but future autonomous vehicles may experience similar security vulnerabilities as EVs, especially with high levels of autonomous processing units and interconnectivity in the application layer (see software-defined batteries for heterogenous battery systems such as EVs and mobile systems proposed by Microsoft, Tesla, University of Massachusetts Amherst, and Columbia University [5]). Already, researchers have expressed their concerns over the importance of securing EV battery systems [42].…”

Section: Attacks Targeting Availabilitymentioning

confidence: 99%

A Security Perspective on Battery Systems of the Internet of Things

et al. 2017

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Battery (sub)systems are used in many systems (systems-of-systems) in the Internet of Things (IoT) ranging from everyday ones (e.g., mobile systems, home appliances, etc.) to safety-critical and/or mission-critical ones (e.g., electrical vehicles, unmanned aerial vehicles, autonomous underwater vehicles, etc.). As these systems become more interconnected with each other and their environments and batteries become more energy dense, the safety risks of using batteries increase. To guarantee effectiveness and prevent potential safety threats (i.e., failure, overheating, explosion), it is not only crucial to ensure that batteries are functioning correctly (via safety circuits and battery management system), but to also prevent security threats that specifically target the battery system from different parts of these systems. A security analysis is necessary for system manufacturers and users to understand what threats and solutions exist for battery system security. In University of Florida, Gainesville, FL 32611, USA this paper, we present a security perspective on battery systems, where we use a layered approach to analyze vulnerabilities, threats, and potential effects. We divide the battery system into the Physical, Battery Management System, and Application layers and use mobile systems and cyber-physical systems as case studies for IoT applications. We then highlight and discuss some existing solutions and mention the potential research directions on battery system security.

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Software defined batteries

Cited by 40 publications

References 22 publications

Power to peep-all: Inference Attacks by Malicious Batteries on Mobile Devices

Power to peep-all: Inference Attacks by Malicious Batteries on Mobile Devices

Enabling Practical Backscatter Communication for On-body Sensors

A Security Perspective on Battery Systems of the Internet of Things

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