Evidence map›Paper›PMID 39820786›Full record

ArticleScientific reports2025

Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms.

Purushottam Singh, Prashant Pranav, Sandip Dutta

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Purushottam SinghDepartment of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India. purushottamsingh@outlook.com.
Prashant PranavDepartment of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.
Sandip DuttaDepartment of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research introduces a novel hybrid cryptographic framework that combines traditional cryptographic protocols with advanced methodologies, specifically Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) and Genetic Algorithms (GA). We evaluated several cryptographic protocols, including AES-ECB, AES-GCM, ChaCha20, RSA, and ECC, against critical metrics such as security level, efficiency, side-channel resistance, and cryptanalysis resistance. Our findings demonstrate that this integrated approach significantly enhances both security and efficiency across all evaluated protocols. Notably, the AES-GCM algorithm exhibited superior performance, achieving minimal computation time and robust side-channel resistance. This study underscores the potential of leveraging machine learning and evolutionary algorithms to advance cryptographic protocol security and efficiency, laying a robust foundation for future advancements in cybersecurity.

Indexed as

Cryptographic protocolsData augmentationGenetic algorithmsSecurity optimizationSide-channel attacksWGAN-GP

Identifiers

PMID39820786
PMCPMC11739502

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.