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Location, Location, Location: Revisiting Modeling and Exploitation for Location-Based Side Channel Leakages

Part of the Lecture Notes in Computer Science book series (LNSC,volume 11923)


Near-field microprobes have the capability to isolate small regions of a chip surface and enable precise measurements with high spatial resolution. Being able to distinguish the activity of small regions has given rise to the location-based side-channel attacks, which exploit the spatial dependencies of cryptographic algorithms in order to recover the secret key. Given the fairly uncharted nature of such leakages, this work revisits the location side-channel to broaden our modeling and exploitation capabilities. Our contribution is threefold. First, we provide a simple spatial model that partially captures the effect of location-based leakages. We use the newly established model to simulate the leakage of different scenarios/countermeasures and follow an information-theoretic approach to evaluate the security level achieved in every case. Second, we perform the first successful location-based attack on the SRAM of a modern ARM Cortex-M4 chip, using standard techniques such as difference of means and multivariate template attacks. Third, we put forward neural networks as classifiers that exploit the location side-channel and showcase their effectiveness on ARM Cortex-M4, especially in the context of single-shot attacks and small memory regions. Template attacks and neural network classifiers are able to reach high spacial accuracy, distinguishing between 2 SRAM regions of 128 bytes each with 100% success rate and distinguishing even between 256 SRAM byte-regions with 32% success rate. Such improved exploitation capabilities revitalize the interest for location vulnerabilities on various implementations, ranging from RSA/ECC with large memory footprint, to lookup-table-based AES with smaller memory usage.


  • Side-channel analysis
  • Location leakage
  • Microprobe
  • Template attack
  • Neural network
  • ARM Cortex-M

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  • DOI: 10.1007/978-3-030-34618-8_10
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    Parallel word processing can be easily included.

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    Unless specified otherwise, we place every word directly next to each other, starting from a random position in the surface.

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    Whether this constitutes an option depends on the situation. If any sort of randomization such as masking or re-keying is present in the device then the adversary is limited in the number of shots that he can combine.

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    The template attack uses the experimental data, while the theoretical SR uses simulated data of the same size and dimensionality.

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    Without knowledge of the chip layout we cannot be fully certain about the distance between memory addresses. Here we assume that the low addresses of the SRAM are sufficiently distant from mid ones, which are approx. 8 KBytes away.

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    The MLP parameters are chosen to maximize the attack success rate (which is equivalent to accuracy).


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We would like to thank Riscure BV, Rafael Boix Carpi, Ilya Kizhvatov and Tin Soerdien for supporting the process of chip decapsulation and scan.

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7 Appendix A

Algorithmic Noise in Tightly-Packed Surfaces. Since countermeasure designers opt often for algorithmic noise countermeasures, we investigate the statistical variance of \(N^{algo}\) for a tightly packed circuit that contains a large number of randomly switching components which try to hide the targeted component. We assume every noise-generating component to have area \(b_i \approx d\), where d is the area of the targeted component Since we assume large \(n_a\), both the noise-generating components as well as the targeted component are small w.r.t. the probe size, i.e. \(d \ll o\). In a tightly packed circuit, the probe area o contains roughly \(\frac{o}{d}\) randomly switching components, i.e. \(n_a \approx \frac{o}{d}\). In this particular scenario, the following formula approximates \(N^{algo}\).

$$\begin{aligned}&\quad \ \ N^{algo} = \sum _{i=1}^{n_a} N^{algo}_i = d \cdot \sum _{i=1}^{n_a} B_i = d \cdot A, \\&\quad B_i \sim Bern(0.5) \text { , } A \sim Binomial(n_a,0.5)\\&\text {Thus, } N^{algo} \xrightarrow [\text {Theorem}]{\text {Central Limit}} Norm(\frac{d \cdot n_a}{2}, \frac{d \cdot n_a}{4} ) \end{aligned}$$

Using the approximation of the Central Limit Theorem, we see that \(Var[N^{algo}]=\frac{d \cdot n_a}{4} = \frac{o}{4}\). Thus, for the tightly-packed, small-component scenario we have established a direct link between the probe area o and the level of algorithmic noise, demonstrating how increasing the probe area induces extra noise.

8 Appendix B

Predicted versus actual values, visualizing the validation set success rate.


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Andrikos, C. et al. (2019). Location, Location, Location: Revisiting Modeling and Exploitation for Location-Based Side Channel Leakages. In: Galbraith, S., Moriai, S. (eds) Advances in Cryptology – ASIACRYPT 2019. ASIACRYPT 2019. Lecture Notes in Computer Science(), vol 11923. Springer, Cham.

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