Scramble Suit: A Profile Differentiation Countermeasure to Prevent Template Attacks

Abstract

Ensuring protection against side channel attacks is a crucial requirement in the design of modern secure embedded systems. Profiled side channel attacks, the class to which template attacks and machine learning attacks belong, derive a model of the side channel behavior of a device identical to the target one, and exploit the said model to extract the key from the target, under the hypothesis that the side channel behaviors of the two devices match. We propose an architectural countermeasure against cross-device profiled attacks which differentiates the side-channel behavior of different instances of the same hardware design, preventing the reuse of a model derived on a device other than the target one. In particular, we describe an instance of our solution providing a protected hardware implementation of the AES block cipher and experimentally validate its resistance against both Bayesian templates and machine learning approaches based on support vector machines also considering different state of the art feature reduction techniques to increase the effectiveness of the profiled attacks. Results show that our countermeasure foils the key retrieval attempts via profiled attacks ensuring a key derivation accuracy equivalent to a random guess.

Design of Side-Channel-Resistant Power Monitors

“…The actual dynamics of the power consumption signal is several orders of magnitude slower with respect to the one of a computing platform and the electricity provider is interested in the actual energy consumption over a period of time, and not to the instantaneous value of the power consumption. Moreover, hiding techniques leveraging noise signals are known to be ineffective against side-channel attacks [7]. [3], [4] demonstrate the possibility of setting up a successful side-channel attack by exploiting the link between the statistics of the architectural performance counters and the data being computed.…”

A Non Profiled and Profiled Side Channel Attack Countermeasure through Computation Interleaving

“…However, it has been experimentally shown in [1]- [5] that building a profile employing measurements coming from multiple device instances (Multi Device Model, MDM) and reducing the measurement setup differences, leads to successful profiled SCAs, regardless of the specific profiling technique (i.e., Bayesian templates or machine learning/neural networks based classifiers). While natural inter-device variability was proven to be manageable by an attacker, the authors of [6] report a countermeasure against profiled attacks named Scramble Suit, which introduces an approach to amplify the said inter-device variability to the point where the profile distortion prevents attacks in a systematic fashion. In [6], such amplification is obtained computing two identical instances of the cipher to be protected, one of which acts on a scrambling secret key derived blending together a device-dependent element while the other employs the actual user-supplied key.…”

“…While natural inter-device variability was proven to be manageable by an attacker, the authors of [6] report a countermeasure against profiled attacks named Scramble Suit, which introduces an approach to amplify the said inter-device variability to the point where the profile distortion prevents attacks in a systematic fashion. In [6], such amplification is obtained computing two identical instances of the cipher to be protected, one of which acts on a scrambling secret key derived blending together a device-dependent element while the other employs the actual user-supplied key. This approach superimposes a device-and-computation dependent noise onto the side channel signal coming from the computation with the user key.…”

On the Effectiveness of True Random Number Generators Implemented on FPGAs

“…However, the security strength of the cryptographic system is directly connected to the quality of the used random numbers, thus highlighting true random number generators (TRNGs) as essential components in the security infrastructure [8]. The correct implementation of TRNGs is indeed paramount to ensuring the effective security of the wide array of cryptographic primitives where they are employed, such as traditional [7] and post-quantum [10,12,19,20] key exchange mechanisms, digital signature schemes [5,11,15], and countermeasures to side-channel attacks [3,21]. A TRNG must therefore produce a sequence of numbers such that the generated values are statistically independent, uniformly distributed, and unpredictable.…”