Pregled bibliografske jedinice broj: 1242644
NeuroSCA: Evolving Activation Functions for Side-Channel Analysis
NeuroSCA: Evolving Activation Functions for Side-Channel Analysis // IEEE access, 11 (2022), 284-299 doi:10.1109/ACCESS.2022.3232064 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1242644 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
NeuroSCA: Evolving Activation Functions for Side-Channel Analysis
Autori
Knežević, Karlo ; Fulir, Juraj ; Jakobović, Domagoj ; Picek, Stjepan ; Đurasević, Marko
Izvornik
IEEE access (2169-3536) 11
(2022);
284-299
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
activation functions ; side-channel analysis ; genetic programming ; neuroevolution
Sažetak
The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more difficult. Consequently, many researchers approach this problem from a different angle, in which instead of selecting an existing activation function, an appropriate activation function is evolved for the problem at hand. In this paper, we demonstrate that evolutionary algorithms can evolve new activation functions for side-channel analysis (SCA), outperforming ReLU and other activation functions commonly applied to that problem. More specifically, we use Genetic Programming to define and explore candidate activation functions (neuroevolution) in the form of mathematical expressions that are gradually improved. Experiments with the ASCAD database show that this approach is highly effective compared to results obtained with standard activation functions and that it can match the state-of-the-art results from the literature. More precisely, the obtained results for the ASCAD fixed key dataset demonstrate that the evolved activation functions can improve the current state-of-the-art by achieving a guessing entropy of 287 for the Hamming weight model and 115 for the Identity leakage model, compared to 447 and 120 obtained in the literature.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Stjepan Picek (autor)
Marko Đurasević (autor)
Karlo Knežević (autor)
Domagoj Jakobović (autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus