Locating Cryptographic Operations in Side-Channel Traces Using Deep Learning

Grunnleggende konsepter

This paper introduces a novel deep-learning technique to accurately locate cryptographic operations within side-channel traces, even in the presence of trace deformations caused by random delay insertion techniques.

Sammendrag

The paper presents a deep-learning approach to identify cryptographic operations in side-channel traces, overcoming challenges posed by random delay countermeasures. The methodology successfully locates and aligns cryptographic operations within the trace, enabling effective attacks against various cryptographic primitives. Experimental results demonstrate the methodology's effectiveness across different settings and encryption algorithms, providing a valuable tool for cybersecurity research and analysis.

Statistikk

"We present, to the best of our knowledge, the first deep-learning approach to locate the COs within the side-channel power trace in presence of a random delay countermeasure."
"Our proposal presents three contributions to the state of the art."
"The segmentation hits score is 100% for every cryptographic algorithm in both scenarios."

Sitater

"We presented a novel technique based on deep learning to identify cryptographic operations in a side-channel trace."
"The experimental results confirmed the validity of the proposed methodology that allows to set up a successful CPA attack."

Viktige innsikter hentet fra

A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces

by Giuseppe Chi... klokken arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19037.pdf

Dypere Spørsmål

How can this deep-learning technique be applied to other areas beyond cybersecurity

This deep-learning technique can be applied to various areas beyond cybersecurity, leveraging its ability to identify patterns and make predictions based on data. In finance, it could be used for fraud detection by analyzing transaction patterns and identifying anomalies. In healthcare, the technique could assist in medical image analysis for diagnosing diseases or predicting patient outcomes. Additionally, in manufacturing, it could optimize processes by analyzing sensor data to predict equipment failures or improve production efficiency.

What are potential drawbacks or limitations of relying solely on deep learning for side-channel analysis

While deep learning shows promise in side-channel analysis, there are potential drawbacks and limitations to consider. One limitation is the need for a significant amount of labeled training data which may not always be readily available especially in scenarios with limited resources or access to diverse datasets. Another drawback is the black-box nature of deep learning models which makes it challenging to interpret how decisions are made within the model leading to issues with transparency and explainability. Moreover, deep learning models can be susceptible to adversarial attacks where small perturbations in input data can lead to incorrect outputs.

How might advancements in hardware technology impact the effectiveness of this methodology

Advancements in hardware technology can significantly impact the effectiveness of this methodology. Improved processing power and memory capabilities would allow for faster training times and more complex neural network architectures resulting in better performance overall. Specialized hardware such as GPUs or TPUs designed specifically for deep learning tasks can further enhance the speed and efficiency of computations involved in locating cryptographic operations within side-channel traces. Additionally, advancements like neuromorphic computing that mimic brain structure could potentially revolutionize how deep learning techniques are implemented offering new opportunities for optimization and scalability.