A Pre-processing Composition for Secret Key Recovery on Android Smartphone

Nakano, Yoshitaka; Souissi, Youssef; Nguyen, Robert; Sauvage, Laurent; Danger, Jean‐Luc; Guilley, Sylvain; Kiyomoto, Shinsaku; Miyake, Yutaka

doi:10.1007/978-3-662-43826-8_6

Cited by 16 publications

(15 citation statements)

References 14 publications

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“…Gebotys et al [50] demonstrated attacks on software implementations of the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) on Java-based PDAs. Later on, Nakano et al [51] attacked ECC and RSA implementations of the default crypto provider (JCE) on Android smartphones, Goller and Sigl [52] attacked RSA implementations on Android, and Belgarric et al [53] attacked the Elliptic Curve Digital Signature Algorithm (ECDSA) implementation of Android's Bouncy Castle. In a similar manner, Genkin et al [54] attacked the OpenSSL implementation of ECDSA on Android and the CommonCrypto implementation of ECDSA on iOS, respectively.…”

Section: A Passive Attacksmentioning

confidence: 99%

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Spreitzer

Moonsamy

Korak

et al. 2018

IEEE Commun. Surv. Tutorials

179

109

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Abstract-Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices.In this paper, we propose a new categorization system for sidechannel attacks, which is necessary as side-channel attacks have evolved significantly since their scientific investigations during the smart card era in the 1990s. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive survey of existing attacks and attack strategies provides valuable insights into the evolving field of side-channel attacks, especially when focusing on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.

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Section: A Passive Attacksmentioning

confidence: 99%

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Spreitzer

Moonsamy

Korak

et al. 2018

IEEE Commun. Surv. Tutorials

179

109

View full text Add to dashboard Cite

show abstract

“…Using invasive access to the device, it is possible to acquire electromagnetic and power measurements with very high fidelity in terms of bandwidth, noise and spatial locality. Such invasive access has been used for key extraction attacks on intentionallynaive RSA implementations [33,45]. A non-invasive attack was shown by Kenworthy and Rohatgi [4,38] on BouncyCastle's RSA implementation running on a smartphone.…”

Section: Overviewmentioning

confidence: 99%

ECDSA Key Extraction from Mobile Devices via Nonintrusive Physical Side Channels

Genkin

Pachmanov

Pipman

et al. 2016

Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

130

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We show that elliptic-curve cryptography implementations on mobile devices are vulnerable to electromagnetic and power side-channel attacks. We demonstrate full extraction of ECDSA secret signing keys from OpenSSL and CoreBitcoin running on iOS devices, and partial key leakage from OpenSSL running on Android and from iOS's Common-Crypto. These non-intrusive attacks use a simple magnetic probe placed in proximity to the device, or a power probe on the phone's USB cable. They use a bandwidth of merely a few hundred kHz, and can be performed cheaply using an audio card and an improvised magnetic probe. * The authors thank Noam Nissan for programming and lab support during the course of this research.

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“…There are three stages at which the EM data can be analysed [31]. Stage one is to compute the Fast Fourier Transform (FFT) over the baseband waveform.…”

Section: Discussionmentioning

confidence: 99%

“…Nakano et al [31] attacked an Android smartphone using low frequency attacks. The smartphone ran at 832 MHz.…”

Section: Electromagnetic Attacksmentioning

confidence: 99%

“…In addition, the research in [34] recently demonstrated the ability to recover partial (12 out of the 16 subkeys) AES-128 cryptographic keys from a Raspberry Pi. As it is a new field the research [27][28][29][30][31][32][33][34] has followed attacks introduced by targeting smartcards, RFID tags, FPGAs and microcontrollers. However, each attack against these higher frequency devices had different approaches with regards to recovery EM information and applying digital filtering techniques to recover sensitive information.…”

Section: Electromagnetic Attacksmentioning

confidence: 99%

See 1 more Smart Citation

Developing an Electromagnetic Noise Generator to Protect a Raspberry PI from Side Channel Analysis

Frieslaar

Irwin

2018

SAIEE Afr. Res. J.

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This research investigates the Electromagnetic (EM) side channel leakage of a Raspberry Pi 2 B+. An evaluation is performed on the EM leakage as the device executes the AES-128 cryptographic algorithm contained in the libcrypto++ library in a threaded environment. Four multi-threaded implementations are evaluated. These implementations are Portable Operating System Interface Threads, C++11 threads, Threading Building Blocks, and OpenMP threads. It is demonstrated that the various thread techniques have distinct variations in frequency and shape as EM emanations are leaked from the Raspberry Pi. It is demonstrated that the AES-128 cryptographic implementation within the libcrypto++ library on a Raspberry Pi is vulnerable to Side Channel Analysis (SCA) attacks. The cryptographic process was seen visibly within the EM spectrum and the data for this process was extracted where digital filtering techniques was applied to the signal. The resultant data was utilised in the Differential Electromagnetic Analysis (DEMA) attack and the results revealed 16 sub-keys that are required to recover the full AES-128 secret key. Based on this discovery, this research introduced a multi-threading approach with the utilisation of Secure Hash Algorithm (SHA) to serve as a software based countermeasure to mitigate SCA attacks. The proposed countermeasure known as the FRIES noise generator executed as a Daemon and generated EM noise that was able to hide the cryptographic implementations and prevent the DEMA attack and other statistical analysis.

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A Pre-processing Composition for Secret Key Recovery on Android Smartphone

Cited by 16 publications

References 14 publications

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

ECDSA Key Extraction from Mobile Devices via Nonintrusive Physical Side Channels

Developing an Electromagnetic Noise Generator to Protect a Raspberry PI from Side Channel Analysis

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