Capacity of the EM Covert/Side-Channel Created by the Execution of Instructions in a Processor

Yilmaz, Baki Berkay; Callan, Robert; Prvulovic, Milos; Zajić, Alenka

doi:10.1109/tifs.2017.2762826

Cited by 30 publications

(11 citation statements)

References 26 publications

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“…It is known that all electronic devices emanate electromagnetic noise when an electric current goes through their circuits 77 . In that same paper, the researchers mention that the processor leaks a different signal when executing each function.…”

Section: Electromagnetic Authenticationmentioning

confidence: 99%

“…When a processor executes a command, it has an electric current flowing in its components. That current varies depending on the data previously stored in it, and, therefore, carries information about the processor contents 77 . As this current creates an electromagnetic wave that can be noticed by nearby sensors, it can be treated as a potential leak source.…”

Section: Electromagnetic Authenticationmentioning

confidence: 99%

“…When processing a program that consists of a loop that repeatedly calculates upon random numbers using functions that leak more information 77 we were able to see a peak of operation, knowing that the time of execution of the program's loop matches the period of the generated EM wave 79 …”

Section: Electromagnetic Authenticationmentioning

confidence: 99%

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Internet of Things device authentication via electromagnetic fingerprints

Souza

Carlson

Ramos

et al. 2020

Engineering Reports

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We quickly approach a future where Internet of Things (IoT) devices are the norm. In this scenario, humans are surrounded by a multitude of heterogeneous devices that assist them in almost every aspect of their daily routines. The realization of this future demands strong authentication guarantees to ensure that these devices are not abused and that their users are not endangered. However, providing authentication for these systems is challenging due to the high heterogeneity of IoT applications. In this paper, we first review several IoT application scenarios and promising authentication methods for each. We identify th e key characteristics of each IoT application scenario, present the strengths and weaknesses of prominent authentication methods from the literature, and review which authentication methods have been proposed in the literature for each application. Then, we present a novel authentication method for IoT based on electromagnetic noise. The key advantage of electromagnetic noise is that any electronic device intrinsically generates electromagnetic noise during normal operation. We extract features from these electromagnetic emanations and use machine learning algorithms to identify devices based on these features. Our method achieves 77% accuracy when identifying devices among a set of seven devices.

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Section: Electromagnetic Authenticationmentioning

confidence: 99%

Section: Electromagnetic Authenticationmentioning

confidence: 99%

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Internet of Things device authentication via electromagnetic fingerprints

Souza

Carlson

Ramos

et al. 2020

Engineering Reports

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“…[1][2][3][4][5][6][7] Side channels are unintentional and asynchronous channels that can leak some sensitive information while performing a computer activity. 8 Many attacks are reported based on power analysis, 1, 2, 9-11 temperature analysis, 12 acoustic emanations, 13,14 electromagnetic (EM) emanations, 7,[15][16][17][18] etc. One of the emerging applications of side-channel analysis is their use for tracking program activities on various code levels such as loops, paths, and basic blocks.…”

Section: Introductionmentioning

confidence: 99%

Instruction level program tracking using electromagnetic emanations

et al. 2019

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Monitoring computer system activities on the instruction level provides more resilience to malware attacks because these attacks can be analyzed better by observing the changes on the instruction level. Assuming the source code is available, many training signals can be collected to track the instruction sequence to detect whether a malware is injected or the system works properly. However, training signals have to be collected with high sampling rate to ensure that the significant features of these signals do not vanish. Since the clock frequencies of the current computer systems are extremely high, we need to have a commercial device with high sampling rate, i.e. 10GHz, which either costs remarkably high, or does not exist. To eliminate the deficiencies regarding the insufficient sampling rate, we propose a method to increase the sampling rate with the moderate commercial devices for training symbols. In that respect, we first generate some random instruction sequences which exist in the inspected source code. Then, these sequences are executed in a for-loop, and emanated electromagnetic (EM) signals from the processor are collected by a commercially available device with moderate sampling rate, i.e. sampling rate is much smaller than the clock frequency. Lastly, we apply a mapping of the gathered samples by utilizing modulo of their timings with respect to execution time of overall instruction sequence. As the final step, we provide some experimental results to illustrate that we successfully track the instruction sequence by applying the proposed approach.

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“…Pequenas variações na resistência intrínseca dos transistores utilizados no processador levam a uma variação da corrente que circula no dispositivo e consequentemente no campo elétrico e magnético gerados. Essas pequenas variações divergem entre dois ou mais dispositivos mas permanecem inalteradas quando o mesmo dispositivoé analisado múltiplas vezes de tal modo que um sinal geradoéúnico de cada dispositivo [Yilmaz et al 2018]. Desse modo, os componentes eletrônicos sãoúnicos e irreplicáveis.…”

Section: Introductionunclassified

Autenticação baseada em ondas eletromagnéticas

Carlson¹,

Gonzaga²,

Oliveira³

et al. 2019

Anais Estendidos Do XIX Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg Estendido 2019)

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O crescente uso de dispositivos inteligentes traz preocupações quanto a segurança das informações trocadas. Métodos de Autenticação nos permitem veriﬁcar a procedência de uma mensagem. Um método utilizado para autenticar uma mensagem é utilizar a assinatura digital do dispositivo. Da mesma forma que os seres humanos possuem características que permitem distinguir uma pessoa de outra, dispositivos eletrônicos possuem diferenças provindas do processo de manufatura. Quando um dispositivo eletrônico é utilizado para gerar algum tipo de mídia, as diferenças podem ser vistas no resultado. Essas características vistas no sinal gerado podem ser utilizadas para extrair uma assinatura digital. Nesse trabalho Arduinos, dispositivos típicos de IoT, foram utilizados para gerar um sinal que foi observado por um rádio e estudado com o intuito de encontrar diferenças que poderiam levar à criação de uma assinatura para cada dispositivo e assim, desenvolver um método de autenticação entre dispositivos IoT e um servidor.

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Capacity of the EM Covert/Side-Channel Created by the Execution of Instructions in a Processor

Cited by 30 publications

References 26 publications

Internet of Things device authentication via electromagnetic fingerprints

Internet of Things device authentication via electromagnetic fingerprints

Instruction level program tracking using electromagnetic emanations

Autenticação baseada em ondas eletromagnéticas

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