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1. X-DeepSCA: Cross-Device Deep Learning Side Channel Attack

   https://doi.org/10.1145/3316781.3317934  

   Das, Debayan; Golder, Anupam; Danial, Josef; Ghosh, Santosh; Raychowdhury, Arijit; Sen, Shreyas (January 2019, DAC '19: Proceedings of the 56th Annual Design Automation Conference 2019)

   null (Ed.)

   This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of > 99.9%, even in presence of significantly higher inter-device variations compared to the inter-key variations. Augmenting traces captured from multiple devices for training and with proper choice of hyper-parameters, the proposed 256-class Deep Neural Network (DNN) learns accurately from the power side-channel leakage of an AES-128 target encryption engine, and an N-trace (N ≤ 10) X-DeepSCA attack breaks different target devices within seconds compared to a few minutes for a correlational power analysis (CPA) attack, thereby increasing the threat surface for embedded devices significantly. Even for low SNR scenarios, the proposed X-DeepSCA attack achieves ∼ 10× lower minimum traces to disclosure (MTD) compared to a traditional CPA. 

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2. A Cross-Platform Cache Timing Attack Framework via Deep Learning

   https://doi.org/10.23919/DATE54114.2022.9774612  

   Ding, Ruyi; Zhang, Ziyue; Zhang, Xiang; Gongye, Cheng; Fei, Yunsi; Ding, Aidong A. (March 2022, IEEE Design Automation and Test in Europe 2-22)

   While deep learning methods have been adopted in power side-channel analysis, they have not been applied to cache timing attacks due to the limited dimension of cache timing data. This paper proposes a persistent cache monitor based on cache line flushing instructions, which runs concurrently to a victim execution and captures detailed memory access patterns in high- dimensional timing traces. We discover a new cache timing side- channel across both inclusive and non-inclusive caches, different from the traditional “Flush+Flush” timing leakage. We then propose a non-profiling differential deep learning analysis strategy to exploit the cache timing traces for key recovery. We further propose a framework for cross-platform cache timing attack via deep learning. Knowledge learned from profiling a common reference device can be transferred to build models to attack many other victim devices, even in different processor families. We take the OpenSSL AES-128 encryption algorithm as an example victim and deploy an asynchronous cache attack. We target three different devices from Intel, AMD, and ARM processors. We examine various scenarios for assigning the teacher role to one device and the student role to other devices, and evaluate the cross- platform deep-learning attack framework. Experimental results show that this new attack is easily extendable to victim devices • and is more effective than attacks without any prior knowledge. 

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3. Charge Based Power Side-Channel Attack Methodology for an Adiabatic Cipher

   https://doi.org/10.3390/electronics10121438  

   Dhananjay, Krithika; Salman, Emre (June 2021, Electronics)

   SIMON is a block cipher developed to provide flexible security options for lightweight hardware applications such as the Internet-of-things (IoT). Safeguarding such resource-constrained hardware from side-channel attacks poses a significant challenge. Adiabatic circuit operation has recently received attention for such applications due to ultra-low power consumption. In this work, a charge-based methodology is developed to mount a correlation power analysis (CPA) based side-channel attack to an adiabatic SIMON core. The charge-based method significantly reduces the attack complexity by reducing the required number of power samples by two orders of magnitude. The CPA results demonstrate that the required measurements-to-disclosure (MTD) to retrieve the secret key of an adiabatic SIMON core is 4× higher compared to a conventional static CMOS based implementation. The effect of increase in the target signal load capacitance on the MTD is also investigated. It is observed that the MTD can be reduced by half if the load driven by the target signal is increased by 2× for an adiabatic SIMON, and by 5× for a static CMOS based SIMON. This sensitivity to target signal capacitance of the adiabatic SIMON can pose a serious concern by facilitating a more efficient CPA attack. 

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4. Key Update Countermeasure for Correlation-Based Side-Channel Attacks

   Yutian Gui, Suyash Mohan (April 2020, Journal of hardware and systems security)

   Side-channel analysis is a non-invasive form of attack that reveals the secret key of the cryptographic circuit by analyzing the leaked physical information. The traditional brute-force and cryptanalysis attacks target the weakness in the encryption algorithm, whereas side-channel attacks use statistical models such as differential analysis and correlation analysis on the leaked information gained from the cryptographic device during the run-time. As a non-invasive and passive attack, the side-channel attack brings a lot of difficulties for detection and defense. In this work, we propose a key update scheme as a countermeasure for power and electromagnetic analysis-based attacks on the cryptographic device. The proposed countermeasure utilizes a secure coprocessor to provide secure key generation and storage in a trusted environment. The experimental results show that the proposed key update scheme can mitigate side-channel attacks significantly. 

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5. EE-ACML: Energy-Efficient Adiabatic CMOS/MTJ Logic for CPA-Resistant IoT Devices

   https://doi.org/10.3390/s21227651  

   Kahleifeh, Zachary; Thapliyal, Himanshu (November 2021, Sensors)

   Internet of Things (IoT) devices have strict energy constraints as they often operate on a battery supply. The cryptographic operations within IoT devices consume substantial energy and are vulnerable to a class of hardware attacks known as side-channel attacks. To reduce the energy consumption and defend against side-channel attacks, we propose combining adiabatic logic and Magnetic Tunnel Junctions to form our novel Energy Efficient-Adiabatic CMOS/MTJ Logic (EE-ACML). EE-ACML is shown to be both low energy and secure when compared to existing CMOS/MTJ architectures. EE-ACML reduces dynamic energy consumption with adiabatic logic, while MTJs reduce the leakage power of a circuit. To show practical functionality and energy savings, we designed one round of PRESENT-80 with the proposed EE-ACML integrated with an adiabatic clock generator. The proposed EE-ACML-based PRESENT-80 showed energy savings of 67.24% at 25 MHz and 86.5% at 100 MHz when compared with a previously proposed CMOS/MTJ circuit. Furthermore, we performed a CPA attack on our proposed design, and the key was kept secret. 

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