scholarly journals Survey of CPU Cache-Based Side-Channel Attacks: Systematic Analysis, Security Models, and Countermeasures

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chao Su ◽  
Qingkai Zeng
Keyword(s):  
Side Channel ◽  
Security Model ◽  
Shared Resources ◽  
Side Channel Attacks ◽  
Privacy And Security ◽  
Systematic Analysis ◽  
Defense Strategies ◽  
Elliptic Curve Cryptosystems ◽  
Modern Computer ◽  
Attack Surface

Privacy protection is an essential part of information security. The use of shared resources demands more privacy and security protection, especially in cloud computing environments. Side-channel attacks based on CPU cache utilize shared CPU caches within the same physical device to compromise the system’s privacy (encryption keys, program status, etc.). Information is leaked through channels that are not intended to transmit information, jeopardizing system security. These attacks have the characteristics of both high concealment and high risk. Despite the improvement in architecture, which makes it more difficult to launch system intrusion and privacy leakage through traditional methods, side-channel attacks ignore those defenses because of the shared hardware. Difficult to be detected, they are much more dangerous in modern computer systems. Although some researchers focus on the survey of side-channel attacks, their study is limited to cryptographic modules such as Elliptic Curve Cryptosystems. All the discussions are based on real-world applications (e.g., Curve25519), and there is no systematic analysis for the related attack and security model. Firstly, this paper compares different types of cache-based side-channel attacks. Based on the comparison, a security model is proposed. The model describes the attacks from four key aspects, namely, vulnerability, cache type, pattern, and range. Through reviewing the corresponding defense methods, it reveals from which perspective defense strategies are effective for side-channel attacks. Finally, the challenges and research trends of CPU cache-based side-channel attacks in both attacking and defending are explored. The systematic analysis of CPU cache-based side-channel attacks highlights the fact that these attacks are more dangerous than expected. We believe our survey would draw developers’ attention to side-channel attacks and help to reduce the attack surface in the future.

Power Side-Channel Analysis of RNS GLV ECC Using Machine and Deep Learning Algorithms

2021 ◽  
Vol 21 (3) ◽  
pp. 1-20
Author(s):  
Mohamad Ali Mehrabi ◽  
Naila Mukhtar ◽  
Alireza Jolfaei
Keyword(s):  
Deep Learning ◽  
Elliptic Curve ◽  
Smart Cities ◽  
Information Leakage ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Public Key Cryptosystems ◽  
Elliptic Curve Cryptosystems ◽  
Hardware Implementations ◽  
Substantial Progress

Many Internet of Things applications in smart cities use elliptic-curve cryptosystems due to their efficiency compared to other well-known public-key cryptosystems such as RSA. One of the important components of an elliptic-curve-based cryptosystem is the elliptic-curve point multiplication which has been shown to be vulnerable to various types of side-channel attacks. Recently, substantial progress has been made in applying deep learning to side-channel attacks. Conceptually, the idea is to monitor a core while it is running encryption for information leakage of a certain kind, for example, power consumption. The knowledge of the underlying encryption algorithm can be used to train a model to recognise the key used for encryption. The model is then applied to traces gathered from the crypto core in order to recover the encryption key. In this article, we propose an RNS GLV elliptic curve cryptography core which is immune to machine learning and deep learning based side-channel attacks. The experimental analysis confirms the proposed crypto core does not leak any information about the private key and therefore it is suitable for hardware implementations.

Evaluation of (power) side-channels in cryptographic implementations

2019 ◽  
Vol 61 (1) ◽  
pp. 15-28
Author(s):  
Florian Bache ◽  
Christina Plump ◽  
Jonas Wloka ◽  
Tim Güneysu ◽  
Rolf Drechsler
Keyword(s):  
Evaluation System ◽  
Evaluation Process ◽  
Physical Reality ◽  
Evaluation Framework ◽  
Side Channel ◽  
Side Channel Attacks ◽  
Side Channels ◽  
Efficient Data ◽  
Attack Surface ◽  
The Internet Of Things

Abstract Side-channel attacks enable powerful adversarial strategies against cryptographic devices and encounter an ever-growing attack surface in today’s world of digitalization and the internet of things. While the employment of provably secure side-channel countermeasures like masking have become increasingly popular in recent years, great care must be taken when implementing these in actual devices. The reasons for this are two-fold: The models on which these countermeasures rely do not fully capture the physical reality and compliance with the requirements of the countermeasures is non-trivial in complex implementations. Therefore, it is imperative to validate the SCA-security of concrete instantiations of cryptographic devices using measurements on the actual device. In this article we propose a side-channel evaluation framework that combines an efficient data acquisition process with state-of-the-art confidence interval based leakage assessment. Our approach allows a sound assessment of the potential susceptibility of cryptographic implementations to side-channel attacks and is robust against noise in the evaluation system. We illustrate the steps in the evaluation process by applying them to a protected implementation of AES.

Countermeasure for Cryptographic Chips to Resist Side-Channel Attacks

2009 ◽  
Vol 19 (11) ◽  
pp. 2990-2998 ◽  
Author(s):  
Tao ZHANG ◽  
Ming-Yu FAN
Keyword(s):  
Side Channel ◽  
Side Channel Attacks

scholarly journals ThermalAttackNet: Are CNNs Making It Easy to Perform Temperature Side-Channel Attack in Mobile Edge Devices?

2021 ◽  
Vol 13 (6) ◽  
pp. 146
Author(s):  
Somdip Dey ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier
Keyword(s):  
Information Flow ◽  
Heat Dissipation ◽  
Side Channel ◽  
Side Channel Attack ◽  
Side Channel Attacks ◽  
Information Flow Control ◽  
On Chip ◽  
Temperature Side ◽  
Over Time ◽  
Memory Efficient

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this paper, we study how computer vision-based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi-processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.

Sign in / Sign up

Export Citation Format

Share Document