Eddie

Nazari, Alireza; Sehatbakhsh, Nader; Alam, Monjur; Zajić, Alenka; Prvulovic, Milos

doi:10.1145/3079856.3080223

Cited by 82 publications

(5 citation statements)

References 60 publications

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“…EM side-channel attacks. Most works studying electromagnetic (EM) side channels have to use a special EM probe to collect EM signals to reverse engineer a neural network [9], monitor program execution [29], [49] from micro-controllers, extract secret keys [3], [4], [11], [25], recognize the security code from the touchscreen [42], and infer keystrokes [32] from smartphones. Other works use a smartphone as a probe to launch attacks, such as monitoring software launches in laptops [20].…”

Section: Related Workmentioning

confidence: 99%

“…Other works use a smartphone as a probe to launch attacks, such as monitoring software launches in laptops [20]. These EM-based attacks are launched in a close proximity, e.g., less than 1cm [3], [4], [9], [11], [25], [29], [42], [49], 2.5-5cm [20], 20-90cm [32]. Different from these works, WISERS leverages a new side channel and uses a COTS smartphone to uncover user interactions on another victim smartphone in a fine-grained and context-aware manner from similar proximity.…”

Section: Related Workmentioning

confidence: 99%

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Uncovering User Interactions on Smartphones via Contactless Wireless Charging Side Channels

Zhang

Zuo

et al. 2023

2023 IEEE Symposium on Security and Privacy (SP)

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Today, there is an increasing number of smartphones supporting wireless charging that leverages electromagnetic induction to transmit power from a wireless charger to the charging smartphone. In this paper, we report a new contactless and context-aware wireless-charging side-channel attack, which captures two physical phenomena (i.e., the coil whine and the magnetic field perturbation) generated during this wireless charging process and further infers the user interactions on the charging smartphone. We design and implement a threestage attack framework, dubbed WISERS, to demonstrate the practicality of this new side channel. WISERS first captures the coil whine and the magnetic field perturbation emitted by the wireless charger, then infers (i) inter-interface switches (e.g., switching from the home screen to an app interface) and (ii) intra-interface activities (e.g., keyboard inputs inside an app) to build user interaction contexts, and further reveals sensitive information. We extensively evaluate the effectiveness of WISERS with popular smartphones and commercial-off-theshelf (COTS) wireless chargers. Our evaluation results suggest that WISERS can achieve over 90.4% accuracy in inferring sensitive information, such as screen-unlocking passcode and app launch. In addition, our study also shows that WISERS is resilient to a list of impact factors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Uncovering User Interactions on Smartphones via Contactless Wireless Charging Side Channels

Zhang

Zuo

et al. 2023

2023 IEEE Symposium on Security and Privacy (SP)

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“…In principle, certain characteristics of the emitted analog signals have a strong correlation to the instructions being executed by the CPU. Thus, numerous side-channel-based anomaly detection approaches have been proposed particularly to provide external protection for embedded devices [1], [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…This is primarily due to the ease of data collection and the robustness of this modality against environmental noise. Nevertheless, when compared to power-based approaches, electromagnetic (EM) based methods are theoretically more advantageous because the EM spectrum offers higher bandwidth, and the EM signals can be sampled at higher rates [3], [6]. Moreover, depending on the type of antenna, the approach can be less invasive as the monitoring can be performed from a distance in real time.…”

Section: Introductionmentioning

confidence: 99%

Towards Scalable Anomaly Detection for Embedded Devices through Synthetic EM Fingerprinting

Vedros¹,

Makrakis²,

Kolias³

et al. 2023

Computer Science, Engineering and Applications

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Embedded devices are omnipresent in modern networks, including those facilitating missioncritical applications. However, due to their constrained nature, novel mechanisms are required to provide external, and non-intrusive defenses. Among such approaches, one that has gained traction is based on analyzing the emanated electromagnetic (EM) signals. Unfortunately, one of the most neglected challenges of this approach is the manual gathering and fingerprinting of the corresponding EM signals. Indeed, even simple programs are comprised of numerous branches, making the fingerprinting stage extremely timeconsuming, and requiring the manual labor of an expert. To address this issue, we propose a framework for generating synthetic EM signals directly from machine code. These subsequent signals can be used to train an anomaly detection system. The advantage of this approach is that it completely removes the need for an elaborate and error-prone fingerprinting stage, thus, increasing the scalability of the protection mechanisms. The experimental evaluations indicate that our method provides above 90% detection accuracy against code injection attacks. Moreover, the proposed methodology inflicts only -1.3% penalty in accuracy for detecting injections of as little as four malicious instructions when compared to the same methods of training on real signals.

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“…In the past, alternative sidechannels have been considered, including the power consumption patterns [3], [4], [5], the thermal footprint [6] or the acoustic signals [7] of devices during their operation. Nevertheless, EM-based approaches [8], [9], [10], [11], [12] offer a comparative advantage since the signals themselves can be captured and analyzed in a completely non-intrusive fashion, i.e., no installation of software in the monitored device is assumed. Moreover, unlike analog signals describing the analysis of power consumption, the EM spectrum offers high bandwidth.…”

Section: Introductionmentioning

confidence: 99%

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

Vedros

Makrakis

Kolias

et al. 2021

2021 Resilience Week (RWS)

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The difficulty in applying traditional security mechanisms in ICS environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to the use of device EM emanations for defense purposes, and especially for the detection of possible anomalous behavior. Such approaches may lead to the development of robust external and non-intrusive anomaly detection mechanisms. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection to identify injection attacks in control logic software in noisy environments. Our study identified that indeed environmental noise might significantly degrade the anomaly detection process. Nevertheless, assuming that signals are captured with high sampling rates, even minor injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. Moreover, experiments in a real-life testbed attest that even single-instruction injections can be detected with nearperfect accuracy in relatively clean environments. Such attacks can still be detected reliably even in noisy environments after the application of noise-elimination techniques.

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Eddie

Cited by 82 publications

References 60 publications

Uncovering User Interactions on Smartphones via Contactless Wireless Charging Side Channels

Uncovering User Interactions on Smartphones via Contactless Wireless Charging Side Channels

Towards Scalable Anomaly Detection for Embedded Devices through Synthetic EM Fingerprinting

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

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