Apple vs. EMA

Haas, Gregor; Aysu, Aydın

doi:10.1145/3489517.3530437

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Symmetric encryption algorithms, i.e., AES and DES, are particularly vulnerable to EM side-channel attacks since they require a secret key to both encrypt and decrypt data [92,94,167]. EM-SCA can be used by attackers to extract information about the secret key, and examples of devices that implement DES and AES, and thus are vulnerable, e.g., smartphones [94], laptops/computers [30], and other portable devices that use encryption to protect sensitive data. The AES algorithm is more effective compared to DES and can process larger block sizes of plaintext with low power dissipation and fast processing time.…”

Section: Discussionmentioning

confidence: 99%

“…Several parts of a smartphone's entire surface can include sensitive information about the computation: Power Management Integrated Circuit (PMIC), external decoupling capacitors, sense resistors, and system-on-chip die. A physical side-channel attack on an ARMv8 Cryptographic Extension (ARM CE)-based AES implementation is presented in a publication by Haas and Aysu [94]. The usual source of cryptographic capability for iPhones, Apple's CoreCrypto library, which uses the ARM CE code, was the target of the attack.…”

Section: A Em-sca On Aes Implementationsmentioning

confidence: 99%

“…In this study, the authors designed and implemented a low-pass filter on MATLAB, which was applied to the EM traces to reduce noise while preserving the original signal's trend and characteristics. The preprocessing of EM traces was deemed necessary due to the weak EM leakage that can result in a significant amount of noise in the collected data [94,149,150]. The implementation of this preprocessing technique improved the efficiency of the CEMA attack.…”

Section: J Data Processingmentioning

confidence: 99%

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Systematic Literature Review of EM-SCA Attacks on Encryption

Zunaidi,

Sayakkara,

Scanlon

2024

Preprint

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Cryptography has become an essential tool in information security, preserving data confidentiality, integrity, and availability. However, despite rigorous analysis, cryptographic algorithms may still be susceptible to attack when used on real-world devices. Side-channel attacks (SCAs) are physical attacks that target cryptographic equipment through quantifiable phenomena such as power consumption, operational times, and EM radiation. These attacks are considered to be a significant threat to cryptography since they compromise the integrity of the algorithm by obtaining the internal cryptographic key of a device by seeing its physical implementation. The literature on SCAs has focused on real-world devices, yet with the growing popularity of sophisticated devices like smartphones, fresh approaches to SCAs are necessary. One such approach is electromagnetic side-channel analysis (EM-SCA), which gathers information by listening to electromagnetic (EM) radiation. EM-SCA has been demonstrated to recover sensitive data like encryption keys and has the potential to identify malicious software, retrieve data, and identify program activity. This study aims to evaluate how well EM-SCA compromises encryption under various application scenarios, as well as examine the role of EM-SCA in digital forensics and law enforcement. Regarding this, addressing the susceptibility of encryption algorithms to EM-SCA approaches can provide digital forensic investigators with the tools they desire to overcome the challenges posed by strong encryption, allowing them to continue playing a crucial role in law enforcement and the justice system. Furthermore, this paper seeks to define the current state of EM-SCA in terms of attacking encryption, the encryption algorithms and encrypted devices that are most vulnerable and resistant to EM-SCA, and the most promising EM-SCA on encryption approaches. This study will provide a comprehensive analysis of EM-SCA in the context of law enforcement and digital forensics and point towards potential directions for further research.

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Section: Discussionmentioning

confidence: 99%

Section: A Em-sca On Aes Implementationsmentioning

confidence: 99%

Section: J Data Processingmentioning

confidence: 99%

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Systematic Literature Review of EM-SCA Attacks on Encryption

Zunaidi,

Sayakkara,

Scanlon

2024

Preprint

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“…Using power-based SCAs, hackers try to figure out the hidden key through the relationship among the cryptographic device's battery usage and the computations that utilize the secret key. 7,8 By sifting through an extensive amount of energy traces to find this little link from noise, introduced a traditional method like DPA to attack the secret key. Yet, as PQKE protocols produce a unique secret key for every execution within the protocol, applying DPA with them is impractical.…”

Section: Introductionmentioning

confidence: 99%

“…Lattice‐based cryptographic works are efficiently used in both software and hardware environments. Using power‐based SCAs, hackers try to figure out the hidden key through the relationship among the cryptographic device's battery usage and the computations that utilize the secret key 7,8 . By sifting through an extensive amount of energy traces to find this little link from noise, introduced a traditional method like DPA to attack the secret key.…”

Section: Introductionmentioning

confidence: 99%

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

Maheswari,

Krishnamurthy

2024

Trans Emerging Tel Tech

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Side‐channel analysis (SCA) is a type of cryptanalytic attack that uses unintended ‘side‐channel’ leakage through the real‐world execution of the cryptographic algorithm to crack a secret key of an embedded system. These side‐channel errors can be discovered through tracking the energy usage of the device performing the technique, electromagnetic radiations while the encryption process, execution time, cache hits/misses, and others. Nowadays, deep learning‐based detection techniques are considered as emerging techniques that have been proposed for attack detection. Deep learning architectures have the ability to learn autonomously and concentrate on difficult features, in contrast to machine learning models. In light of these factors, the work's motive is thought to be the proposal of a deep learning‐based attack detection method. Many methods are used to decrease these assaults, however, the majority of them are inefficient and time‐demanding. In order to address these challenges, this study employs a novel deep learning‐based methodology. Pre‐processing, feature extraction, and SCA classification are the three stages of the approach proposed in this work. First, pre‐processing is used to remove unnecessary information and improve the quality of the input using data cleaning and min‐max normalization. The previously processed data are then fed as input into the proposed hybrid deep learning architecture. A Deep Residual Capsule Auto‐Encoder (DR_CAE) model is introduced in the proposed study. The deep residual neural network‐50 (DRNN‐50) is utilized to extract relevant features in this case, while the side channel analysis is done by using capsule auto‐encoder (CAE). The parameters of the proposed model are adjusted using the modified white shark optimization (MWSO) technique to improve its performance. In the results section, the proposed model is compared to various existing models in terms of accuracy, precision, recall, F‐measures, time, and so on. The proposed framework has an accuracy of 98.802%, F‐measures of 98.801%, kappa coefficient of 97.6%, the precision value of 98.81%, and recall value of 98.80%.

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Multi-armed SPHINCS$$^{+}$$

Banegas,

Caullery

2023

Lecture Notes in Computer Science

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Apple vs. EMA

Cited by 8 publications

References 11 publications

Systematic Literature Review of EM-SCA Attacks on Encryption

Systematic Literature Review of EM-SCA Attacks on Encryption

A profiled side‐channel attack detection using deep learning model with capsule auto‐encoder network

Multi-armed SPHINCS$$^{+}$$

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