Power Side Channel Attacks: Where Are We Standing?

Taouil, Mottaqiallah; Aljuffri, Abdullah; Hamdioui, Said

doi:10.1109/dtis53253.2021.9505075

Cited by 8 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2018 Benadjila, Prouff, and colleagues [25] presented the first comprehensive study of deep learning (DL) methods used in SCA and introduced an opensource dataset consisting of electromagnetic emanation (EM) traces of a software, masked AES implementation, as a benchmark for future researchers interested in DL. In recent years, the usage of ML for SCA against public-key cryptography has only been mentioned in [26][27][28]. The only extensive summary can be found in the state-of-the-art chapter of Hettwer's doctoral thesis [29], where he was able to list 12 publications concerning the usage of ML in SCA against ECC and RSA.…”

Section: Related Workmentioning

confidence: 99%

Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning

Aftowicz,

Kabin,

Dyka

et al. 2024

Future Internet

View full text Add to dashboard Cite

While IoT technology makes industries, cities, and homes smarter, it also opens the door to security risks. With the right equipment and physical access to the devices, the attacker can leverage side-channel information, like timing, power consumption, or electromagnetic emanation, to compromise cryptographic operations and extract the secret key. This work presents a side channel analysis of a cryptographic hardware accelerator for the Elliptic Curve Scalar Multiplication operation, implemented in a Field-Programmable Gate Array and as an Application-Specific Integrated Circuit. The presented framework consists of initial key extraction using a state-of-the-art statistical horizontal attack and is followed by regularized Artificial Neural Networks, which take, as input, the partially incorrect key guesses from the horizontal attack and correct them iteratively. The initial correctness of the horizontal attack, measured as the fraction of correctly extracted bits of the secret key, was improved from 75% to 98% by applying the iterative learning.

show abstract

Section: Related Workmentioning

confidence: 99%

Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning

Aftowicz,

Kabin,

Dyka

et al. 2024

Future Internet

View full text Add to dashboard Cite

show abstract

“…Cryptographic primitives have been shown to be highly vulnerable to power Side-Channel Analysis (SCA) attacks [11], among which Correlation Power Analysis (CPA) [12] has received the lion's share of attention where the adversary retrieves the secret keys via evaluating the Pearson's correlation between the extracted power traces and the result of a hypothetical power function depending on the guessed key. Thereby, deploying an appropriate leakage model is of utmost importance to successfully retrieve the correct key.…”

Section: Preliminaries a Power Analysis Attackmentioning

confidence: 99%

Leakage Power Analysis in Different S-Box Masking Protection Schemes

Bahrami

Ebrahimabadi

Danger

et al. 2022

2022 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

View full text Add to dashboard Cite

show abstract

“…The breach can be made through these nodes via active or passive attacks [26]. Passive attacks include Side-Channel Attacks (SCA) that is well researched, to break even the complex cryptosystems, where mostly the device vulnerabilities are exploited to obtain the security parameters [24]. Active (fault) injections like laser, EM, CG, etc.…”

Section: Motivationmentioning

confidence: 99%

Multi-source Fault Injection Detection Using Machine Learning and Sensor Fusion

Shrivastwa¹,

Guilley²,

Danger³

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Fault attacks have raised serious concern with the growing amount of connected devices. Even a small vulnerability might compromise a complete network. It is therefore important to secure all the devices in the connected architecture. A solution to this problem is presented in this paper where we provide a hardware framework, called Smart Monitor, that utilizes a set of sensors (digital or physical) placed on the chip alongside the security target to be protected. The framework continuously monitors the status of the sensors and its Artificial Intelligence (AI) core produces two outputs viz. presence of a fault and type of detected perturbation. The types of attack sources can be electromagnetic, clock-glitch, laser, temperature, etc. In this work we utilize Electro-Magnetic (EM) and Clock-Glitch (CG) as sources of fault injections. Both attacks are performed in multiple settings to increase attack diversity. The framework is able to detect the presence of an attack with 92% accuracy for mixed or multiple attack sources, and further classify the type of perturbation with 78% accuracy keeping the false positive rate at 0%. Overall, this two-stage detection framework is a cost-effective countermeasure that can be deployed easily in any integrated circuit to safeguard against multiple fault attacks. The AI core is further evaluated for consistency in performance on hardware using High Level Synthesis (HLS), as a proof-of-concept, emulating real-world scenario.

show abstract

Power Side Channel Attacks: Where Are We Standing?

Cited by 8 publications

References 34 publications

Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning

Non-Profiled Unsupervised Horizontal Iterative Attack against Hardware Elliptic Curve Scalar Multiplication Using Machine Learning

Leakage Power Analysis in Different S-Box Masking Protection Schemes

Multi-source Fault Injection Detection Using Machine Learning and Sensor Fusion

Contact Info

Product

Resources

About