ANALYZING AES POWER TRACES FOR SIDE-CHANNEL ATTACK: GENERATION, CLASSIFICATION, KEY DEDUCTION, AND MITIGATION STRATEGIES
Date
2024-08-06
Authors
Rajeev, Keerthana
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Abstract
Side-channel attacks (SCAs) exploit leakages from a system’s physical implementation, like acoustic signals, electromagnetic, and power emissions, to deduce sensitive
information, thus bypassing traditional security measures. SCAs appeal to hackers
due to their non-invasive nature and low cost and thereby necessitate robust countermeasures.
The Advanced Encryption Standard (AES) is a widely used symmetric key cryptography known for its robustness, but it remains susceptible to SCAs. This research
analyzes power traces to identify vulnerabilities, classifies power traces based on AES
implementation, employs Deep Learning(DL) models to deduce cryptographic keys,
and develops mitigation strategies against SCAs.
We generated and analyzed real and synthetic power traces from masked AES implementations using a Syscomp waveform generator and a Python script. Techniques
like Fast Fourier Transform (FFT), Wavelet transform, and linear regression were used
to correlate the traces. Power traces from the AES Power Trace (AES PT) dataset
were classified into three AES implementations using feature extraction techniques
and Support Vector Machine (SVM) classification based on statistical properties from
Principal component analysis (PCA).We used hashing and metadata techniques retrieved original power traces from the feature set.
The study used ANSSI Side-channel Analysis Database (ASCAD) and adopted
deep learning models for key deduction: Residual Networks were transformed into
ResTraceNet using 1D convolutional layers, and Gated Recurrent Units (GRUs) were
modified into GRUTrace to process 1D power traces. These models deduced one key
byte using only 100 power traces, achieving testing accuracies of 96.68% and 96.28%.
We proposed a mitigation strategy involving structured masking and Gaussian noise
to obscure relationships between cryptographic keys and power consumption patterns.
Our proposed research provides a comprehensive analysis of AES power traces,
using DL models to perform feature extraction, classify and deduce cryptographic
keys, and proposes mitigation techniques to enhance defenses against SCAs.
Description
This research focuses on identifying vulnerabilities in the Advanced Encryption Standard (AES) algorithm exposed by side-channel attacks (SCAs). It involves generating real and synthetic power traces, classifying these traces, and deducing cryptographic keys using deep learning techniques. Additionally, the study explores various mitigation strategies to combat SCAs.
Keywords
Advanced Encryption Standard, Side-channel attacks, Deep learning