Clustering Algorithms for Non-Profiled Single-Execution Attacks on Exponentiations

Most implementations of public key cryptography employ exponentiation algorithms. Side-channel attacks on secret exponents are typically bound to the leakage of single executions due to cryptographic protocols or side-channel countermeasures such as blinding. We propose for the first time, to use a well-established class of algorithms, i.e. unsupervised cluster classification algorithms such as the k-means algorithm to attack cryptographic exponentiations and recover secret exponents without any prior profiling, manual tuning or leakage models. Not requiring profiling is of significant advantage to attackers, as are well-established algorithms. The proposed non-profiled single-execution attack is able to exploit any available single-execution leakage and provides a straight-forward option to combine simultaneous measurements to increase the available leakage. We present empirical results from attacking an FPGA-based elliptic curve scalar multiplication using the k-means clustering algorithm and successfully exploit location-based leakage from high-resolution electromagnetic field measurements to achieve a low remaining brute-force complexity of the secret exponent. A simulated multi-channel measurement even enables an error-free recovery of the exponent

How Diversity Affects Deep-Learning Side-Channel Attacks

Deep learning side-channel attacks are an emerging threat to the security of implementations of cryptographic algorithms. The attacker first trains a model on a large set of side-channel traces captured from a chip with a known key. The trained model is then used to recover the unknown key from a few traces captured from a victim chip. The first successful attacks have been demonstrated recently. However, they typically train and test on power traces captured from the same device. In this paper, we show that it is important to train and test on traces captured from different boards and using diverse implementations of the cryptographic algorithm under attack. Otherwise, it is easy to overestimate the classification accuracy. For example, if we train and test an MLP model on power traces captured from the same board, we can recover all key byte values with 96% accuracy from a single trace. However, the single-trace attack accuracy drops to 2.45% if we test on traces captured from a board different from the one we used for training, even if both boards carry identical chips

Clustering Algorithms for Non-Profiled Single-Execution Attacks on Exponentiations

Abstract. Most implementations of public key cryptography employ exponentiation algorithms. Side-channel attacks on secret exponents are typically bound to the leakage of single executions because of cryptographic protocols or side-channel countermeasures such as blinding. We propose a new class of algorithms, i.e. unsupervised cluster classification algorithms, to attack cryptographic exponentiations and recover secret exponents without any prior profiling or heuristic leakage models. Not requiring profiling is a significant advantage to attackers. In fact, the proposed non-profiled single-execution attack is able to exploit any available single-execution leakage and provides a straight-forward option to combine simultaneous measurements to improve the signal-to-noise ratio of available leakage. We present empirical results from attacking an elliptic curve scalar multiplication and exploit location-based leakage from high-resolution electromagnetic field measurements without prior profiling. Individual measurements lead to a sufficiently low remaining brute-force complexity of the secret exponent. An errorless recovery of the exponent is achieved after a combination of few measurements

Survey for Performance & Security Problems of Passive Side-channel Attacks Countermeasures in ECC

The main objective of the Internet of Things is to interconnect everything around us to obtain information which was unavailable to us before, thus enabling us to make better decisions. This interconnection of things involves security issues for any Internet of Things key technology. Here we focus on elliptic curve cryptography (ECC) for embedded devices, which offers a high degree of security, compared to other encryption mechanisms. However, ECC also has security issues, such as Side-Channel Attacks (SCA), which are a growing threat in the implementation of cryptographic devices. This paper analyze the state-of-the-art of several proposals of algorithmic countermeasures to prevent passive SCA on ECC defined over prime fields. This work evaluates the trade-offs between security and the performance of side-channel attack countermeasures for scalar multiplication algorithms without pre-computation, i.e. for variable base point. Although a number of results are required to study the state-of-the-art of side-channel attack in elliptic curve cryptosystems, the interest of this work is to present explicit solutions that may be used for the future implementation of security mechanisms suitable for embedded devices applied to Internet of Things. In addition security problems for the countermeasures are also analyzed