Evidence map›Paper›PMID 36236428›Full record

ArticleSensors (Basel, Switzerland)2022

Best Fit DNA-Based Cryptographic Keys: The Genetic Algorithm Approach.

Pratyusa Mukherjee, Hitendra Garg, Chittaranjan Pradhan, Soumik Ghosh, Subrata Chowdhury, Gautam Srivastava

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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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

6 authors.

Pratyusa MukherjeeSchool of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneshwar 751024, India.
Hitendra GargDepartment of Computer Engineering and Applications, GLA University, Mathura 281406, India.ORCID 0000-0002-4273-2328
Chittaranjan PradhanSchool of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneshwar 751024, India.
Soumik GhoshSiemens Technology Services Private Limited, Mumbai 560100, India.
Subrata ChowdhurySri Venkateswara College of Engineering Technology, Chittoor 517127, India.
Gautam SrivastavaDepartment of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada.ORCID 0000-0001-9851-4103

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.

Indexed as

AlgorithmsResearch DesignDNADNAbest keydata encryptionDNA cryptographygenetic algorithmkey generation

Identifiers

PMID36236428
PMCPMC9573340

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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.