scholarly journals Electromagnetic and Power Side-Channel Analysis: Advanced Attacks and Low-Overhead Generic Countermeasures through White-Box Approach

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 30
Author(s):  
Debayan Das ◽  
Shreyas Sen
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Side Channel ◽  
Secret Key ◽  
Embedded Devices ◽  
Side Channel Analysis ◽  
Root Cause ◽  
Channel Analysis ◽  
Metal Layers ◽  
A Current

Electromagnetic and power side-channel analysis (SCA) provides attackers a prominent tool to extract the secret key from the cryptographic engine. In this article, we present our cross-device deep learning (DL)-based side-channel attack (X-DeepSCA) which reduces the time to attack on embedded devices, thereby increasing the threat surface significantly. Consequently, with the knowledge of such advanced attacks, we performed a ground-up white-box analysis of the crypto IC to root-cause the source of the electromagnetic (EM) side-channel leakage. Equipped with the understanding that the higher-level metals significantly contribute to the EM leakage, we present STELLAR, which proposes to route the crypto core within the lower metals and then embed it within a current-domain signature attenuation (CDSA) hardware to ensure that the critical correlated signature gets suppressed before it reaches the top-level metal layers. CDSA-AES256 with local lower metal routing was fabricated in a TSMC 65 nm process and evaluated against different profiled and non-profiled attacks, showing protection beyond 1B encryptions, compared to ∼10K for the unprotected AES. Overall, the presented countermeasure achieved a 100× improvement over the state-of-the-art countermeasures available, with comparable power/area overheads and without any performance degradation. Moreover, it is a generic countermeasure and can be used to protect any crypto cores while preserving the legacy of the existing implementations.

scholarly journals Plaintext: A Missing Feature for Enhancing the Power of Deep Learning in Side-Channel Analysis?

2020 ◽  
pp. 49-85
Author(s):  
Anh-Tuan Hoang ◽  
Neil Hanley ◽  
Maire O’Neill
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Body ◽  
Side Channel ◽  
Secret Key ◽  
Side Channel Analysis ◽  
Filter Kernel ◽  
Secret Keys ◽  
Channel Analysis ◽  
Template Attacks

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various model architectures for object classification. Straight-forward application of DL to side-channel analysis (SCA) has already shown promising success, with experimentation on open-source variable key datasets showing that secret keys can be revealed with 100s traces even in the presence of countermeasures. This paper aims to further improve the application of DL for SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a new model, CNN-based model with Plaintext feature extension (CNNP) together with multiple convolutional filter kernel sizes and structures with deeper and narrower neural networks, which has empirically proven its effectiveness by outperforming reference profiling attack methods such as template attacks (TAs), convolutional neural networks (CNNs) and multilayer perceptron (MLP) models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 40 attack traces in comparison with order of 100s traces required by straightforward machine learning (ML) application. During the profiling stage an attacker needs no additional knowledge on the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key. Additionally, no heuristic pre-processing is required in order to break the high-order masking countermeasures of the target implementation.

scholarly journals SITM: See-In-The-Middle Side-Channel Assisted Middle Round Differential Cryptanalysis on SPN Block Ciphers

2019 ◽  
pp. 95-122
Author(s):  
Shivam Bhasin ◽  
Jakub Breier ◽  
Xiaolu Hou ◽  
Dirmanto Jap ◽  
Romain Poussier ◽  
...  
Keyword(s):  
Block Cipher ◽  
State Of The Art ◽  
Block Ciphers ◽  
Differential Cryptanalysis ◽  
Side Channel ◽  
Secret Key ◽  
Side Channel Analysis ◽  
Cryptographic Algorithms ◽  
Channel Analysis ◽  
Symmetric Block

Side-channel analysis constitutes a powerful attack vector against cryptographic implementations. Techniques such as power and electromagnetic side-channel analysis have been extensively studied to provide an efficient way to recover the secret key used in cryptographic algorithms. To protect against such attacks, countermeasure designers have developed protection methods, such as masking and hiding, to make the attacks harder. However, due to significant overheads, these protections are sometimes deployed only at the beginning and the end of encryption, which are the main targets for side-channel attacks.In this paper, we present a methodology for side-channel assisted differential cryptanalysis attack to target middle rounds of block cipher implementations. Such method presents a powerful attack vector against designs that normally only protect the beginning and end rounds of ciphers. We generalize the attack to SPN based ciphers and calculate the effort the attacker needs to recover the secret key. We provide experimental results on 8-bit and 32-bit microcontrollers. We provide case studies on state-of-the-art symmetric block ciphers, such as AES, SKINNY, and PRESENT. Furthermore, we show how to attack shuffling-protected implementations.

Profiling Attacks against ECC: Side Channel Analysis Based on Deep Learning for Curve-25519

2021 ◽  
Author(s):  
Jiajun Xu ◽  
Meng Li ◽  
Lixin Liang ◽  
Yiwei Zhang ◽  
Shaohua Xiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Recent advances in deep learning‐based side‐channel analysis

ETRI Journal ◽  
2020 ◽  
Vol 42 (2) ◽  
pp. 292-304 ◽  
Author(s):  
Sunghyun Jin ◽  
Suhri Kim ◽  
HeeSeok Kim ◽  
Seokhie Hong
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Recent Advances ◽  
Channel Analysis

scholarly journals Detecting Faults in Inner-Product Masking Scheme - IPM-FD: IPM with Fault Detection

10.29007/fv2n ◽  
2019 ◽  
Author(s):  
Wei Cheng ◽  
Claude Carlet ◽  
Kouassi Goli ◽  
Jean-Luc Danger ◽  
Sylvain Guilley
Keyword(s):  
Fault Detection ◽  
Fault Injection ◽  
Inner Product ◽  
Side Channel ◽  
Embedded Devices ◽  
Side Channel Analysis ◽  
Injection Attacks ◽  
Word Level ◽  
Channel Analysis ◽  
Fault Injection Attacks

Side-channel analysis and fault injection attacks are two typical threats to cryptographic implementations, especially in modern embedded devices. Thus there is an insistent demand for dual side-channel and fault injection protections. As it is known, masking scheme is a kind of provable countermeasures against side-channel attacks. Recently, inner product masking (IPM) was proposed as a promising higher-order masking scheme against side-channel analysis, but not for fault injection attacks. In this paper, we devise a new masking scheme named IPM-FD. It is built on IPM, which enables fault detection. This novel masking scheme has three properties: the security orders in the word-level probing model, bit-level probing model, and the number of detected faults. IPM-FD is proven secure both in the word-level and in the bit-level probing models, and allows for end-to-end fault detection against fault injection attacks.Furthermore, we illustrate its security order by linking it to one defining parameters of linear code, and show its implementation cost by applying IPM-FD to AES-128.

Deep Learning Techniques for Side-Channel Analysis

2021 ◽  
pp. 255-269
Author(s):  
Varsha Satheesh Kumar ◽  
S. Dillibabu Shanmugam ◽  
N. Sarat Chandra Babu
Keyword(s):  
Deep Learning ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Learning Techniques ◽  
Channel Analysis

scholarly journals A novel side-channel analysis for physical-domain security in cyber-physical systems

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986786 ◽  
Author(s):  
Min Wang ◽  
Kama Huang ◽  
Yi Wang ◽  
Zhen Wu ◽  
Zhibo Du
Keyword(s):  
Information Leakage ◽  
Cyber Physical Systems ◽  
Cyber Attacks ◽  
Side Channel ◽  
Learning Technology ◽  
Physical Domain ◽  
Secret Key ◽  
Side Channel Analysis ◽  
Physical Systems ◽  
Channel Analysis

Security of cyber-physical systems against cyber attacks is an important yet challenging problem. Cyber-physical systems are prone to information leakage from the physical domain. The analog emissions, such as magnetic and power, can turn into side channel revealing valuable data, even the crypto key of the system. Template attack is a popular type of side-channel analysis using machine learning technology. Malicious attackers can use template attack to profile the analog emission, then recover the secret key of the system. But conventional template attack requires that the adversary has access to an identical experiment device that he can program to his choice. This study proposes a novel side-channel analysis for physical-domain security in cyber-physical systems. Our contributions are the following three points: (1) Major peak region method for finding points of interests correctly is proposed. (2) A method for establishing templates on the basis of those points of interest still without requiring knowledge of the key is proposed. Several techniques are proposed to improve the quality of the templates as well. (3) A method for choosing attacking traces is proposed to significantly improve the attacking efficiency. Our experiments on three devices show that the proposed method is significantly more effective than conventional template attack. By doing so, we will highlight the importance of performing similar analysis during design time to secure the cyber-physical system.

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