Exploitation of Unintentional Information Leakage from Integrated Circuits

Unintentional electromagnetic emissions are used to recognize or verify the identity of a unique integrated circuit (IC) based on fabrication process-induced variations in a manner analogous to biometric human identification. The effectiveness of the technique is demonstrated through an extensive empirical study, with results presented indicating correct device identification success rates of greater than 99:5%, and average verification equal error rates (EERs) of less than 0:05% for 40 near-identical devices. The proposed approach is suitable for security applications involving commodity commercial ICs, with substantial cost and scalability advantages over existing approaches. A systematic leakage mapping methodology is also proposed to comprehensively assess the information leakage of arbitrary block cipher implementations, and to quantitatively bound an arbitrary implementation\u27s resistance to the general class of differential side channel analysis techniques. The framework is demonstrated using the well-known Hamming Weight and Hamming Distance leakage models, and approach\u27s effectiveness is demonstrated through the empirical assessment of two typical unprotected implementations of the Advanced Encryption Standard. The assessment results are empirically validated against correlation-based differential power and electromagnetic analysis attacks

Similar operation template attack on RSA-CRT as a case study

A template attack, the most powerful side-channel attack methods, usually first builds the leakage profiles from a controlled profiling device, and then uses these profiles to recover the secret of the target device. It is based on the fact that the profiling device shares similar leakage characteristics with the target device. In this study, we focus on the similar operations in a single device and propose a new variant of the template attack, called the similar operation template attack (SOTA). SOTA builds the models on public variables (e.g., input/output) and recovers the values of the secret variables that leak similar to the public variables. SOTA’s advantage is that it can avoid the requirement of an additional profiling device. In this study, the proposed SOTA method is applied to a straightforward RSA-CRT implementation. Because the leakage is (almost) the same in similar operations, we reduce the security of RSA-CRT to a hidden multiplier problem (HMP) over GF(q), which can be solved byte-wise using our proposed heuristic algorithm. The effectiveness of our proposed method is verified as an entire prime recovery procedure in a practical leakage scenario

SNIFF: Reverse Engineering of Neural Networks with Fault Attacks

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a change of value that is currently being computed. They can be realized by various fault injection techniques, ranging from clock/voltage glitching to application of lasers to rowhammer. In this paper we explore the possibility to reverse engineer neural networks with the usage of fault attacks. SNIFF stands for sign bit flip fault, which enables the reverse engineering by changing the sign of intermediate values. We develop the first exact extraction method on deep-layer feature extractor networks that provably allows the recovery of the model parameters. Our experiments with Keras library show that the precision error for the parameter recovery for the tested networks is less than $10^{-13}$ with the usage of 64-bit floats, which improves the current state of the art by 6 orders of magnitude. Additionally, we discuss the protection techniques against fault injection attacks that can be applied to enhance the fault resistance

Side-channel Analysis of Subscriber Identity Modules

Subscriber identity modules (SIMs) contain useful forensic data but are often locked with a PIN code that restricts access to this data. If an invalid PIN is entered several times, the card locks and may even destroy its stored data. This presents a challenge to the retrieval of data from the SIM when the PIN is unknown. The field of side-channel analysis (SCA) collects, identifies, and processes information leaked via inadvertent channels. One promising side-channel leakage is that of electromagnetic (EM) emanations; by monitoring the SIM\u27s emissions, it may be possible to determine the correct PIN to unlock the card. This thesis uses EM SCA techniques to attempt to discover the SIM card\u27s PIN. The tested SIM is subjected to simple and differential electromagnetic analysis. No clear data dependency or correlation is apparent. The SIM does reveal information pertaining to its validation routine, but the value of the card\u27s stored PIN does not appear to leak via EM emissions. Two factors contributing to this result are the black-box nature of PIN validation and the hardware and software SCA countermeasures. Further experimentation on SIMs with known operational characteristics is recommended to determine the viability of future SCA attacks on these devices

Side channel attacks on smart home systems: A short overview

This paper provides an overview on side-channel attacks with emphasis on vulnerabilities in the smart home. Smart homes are enabled by the latest developments in sensors, communication technologies, internet protocols, and cloud services. The goal of a smart home is to have smart household devices collaborate without involvement of residents to deliver the variety of services needed for a higher quality of life. However, security and privacy challenges of smart homes have to be overcome in order to fully realize the smart home. Side channel attacks assume data is always leaking, and leakage of data from a smart home reveals sensitive information. This paper starts by reviewing side-channel attack categories, then it gives an overview on recent attack studies on different layers of a smart home and their malicious goals

Enhancing Electromagnetic Side-Channel Analysis in an Operational Environment

Side-channel attacks exploit the unintentional emissions from cryptographic devices to determine the secret encryption key. This research identifies methods to make attacks demonstrated in an academic environment more operationally relevant. Algebraic cryptanalysis is used to reconcile redundant information extracted from side-channel attacks on the AES key schedule. A novel thresholding technique is used to select key byte guesses for a satisfiability solver resulting in a 97.5% success rate despite failing for 100% of attacks using standard methods. Two techniques are developed to compensate for differences in emissions from training and test devices dramatically improving the effectiveness of cross device template attacks. Mean and variance normalization improves same part number attack success rates from 65.1% to 100%, and increases the number of locations an attack can be performed by 226%. When normalization is combined with a novel technique to identify and filter signals in collected traces not related to the encryption operation, the number of traces required to perform a successful attack is reduced by 85.8% on average. Finally, software-defined radios are shown to be an effective low-cost method for collecting side-channel emissions in real-time, eliminating the need to modify or profile the target encryption device to gain precise timing information