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Uncharted Territory: Energy Attacks in the Battery-less Internet of Things
Cryptography and Security · Submitted 17 Apr 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Through an independent exploratory study and a follow-up systematic analysis, we demonstrate that by exerting limited control on ambient energy one can create situations of livelock, denial of service, and priority inversion, without physical device access.
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The abstract, sentence by sentence
We study how ambient energy harvesting may be used as an attack vector in the battery-less Internet of Things (IoT). Battery-less IoT devices rely on ambient energy harvesting and are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging energy buffers. Through an independent exploratory study and a follow-up systematic analysis, we demonstrate that by exerting limited control on ambient energy one can create situations of livelock, denial of service, and priority inversion, without physical device access. We call these situations energy attacks. Using concepts of approximate intermittent computing and machine learning, we design a technique that can detect energy attacks with 92%+ accuracy, that is, up to 37% better than the baselines, and with up to one fifth of their energy overhead. Crucially, by design, our technique does not cause any additional energy failure compared to the regular intermittent processing. We conclude with directions to inspire defense techniques and a discussion on the feasibility of energy attacks.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We study how ambient energy harvesting may be used as an attack vector in the battery-less Internet of Things (IoT). | contribution 0.97 | 0.79 | 0.06 | hedged possibility 0.94 |
| s1 | Battery-less IoT devices rely on ambient energy harvesting and are employed in a multitude of applications, including safety-critical ones such as biomedical implants. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging energy buffers. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s3 | Through an independent exploratory study and a follow-up systematic analysis, we demonstrate that by exerting limited control on ambient energy one can create situations of livelock, denial of service, and priority inversion, without physical device access. | result 0.79 | 0.96 | 0.58 | asserted 1.00 |
| s4 | We call these situations energy attacks. | contribution 0.67 | 0.66 | 0.07 | asserted 0.95 |
| s5 | Using concepts of approximate intermittent computing and machine learning, we design a technique that can detect energy attacks with 92%+ accuracy, that is, up to 37% better than the baselines, and with up to one fifth of their energy overhead. | result 0.44 | 0.96 | 0.27 | asserted 1.00 |
| s6 | Crucially, by design, our technique does not cause any additional energy failure compared to the regular intermittent processing. | result 0.84 | 0.91 | 0.12 | asserted 1.00 |
| s7 | We conclude with directions to inspire defense techniques and a discussion on the feasibility of energy attacks. | future work 1.00 | 0.48 | 0.36 | unclear 0.81 |
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Original abstract
We study how ambient energy harvesting may be used as an attack vector in the battery-less Internet of Things (IoT). Battery-less IoT devices rely on ambient energy harvesting and are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging energy buffers. Through an independent exploratory study and a follow-up systematic analysis, we demonstrate that by exerting limited control on ambient energy one can create situations of livelock, denial of service, and priority inversion, without physical device access. We call these situations energy attacks. Using concepts of approximate intermittent computing and machine learning, we design a technique that can detect energy attacks with 92%+ accuracy, that is, up to 37% better than the baselines, and with up to one fifth of their energy overhead. Crucially, by design, our technique does not cause any additional energy failure compared to the regular intermittent processing. We conclude with directions to inspire defense techniques and a discussion on the feasibility of energy attacks.
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How the literature uses it
1 arXiv paper cite this in 6 sentences
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Cites it as support 1 paper
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Experimental evidence exists that shows how intermittently-computing IoT devices are vulnerable to energy attacks [ref] , that is, energy harvesting can be used as an attack vector.
Application-aware Energy Attack Mitigation in the Battery-less Internet of Things · Introduction · 0.96
What it cites
Cites as support 1 paper
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We elect to use support vector machines in the first place because of their extreme accuracy for binary classification [ref] , which is precisely our case, and their optimal trade-off between accuracy of classification and resource consumption [ref] .
Benchmarking TinyML Systems: Challenges and Direction · Design Space · 0.96
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