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Genomik verilerin güvenliği için transformer ağı destekli DNA şifreleme

Transformer network-enabled DNA encryption for genomic data security

Alev Kaya(FIRAT UNIVERSITY)0000-0002-3544-5267
İbrahim Türkoğlu(FIRAT ÜNİVERSİTESİ)0000-0003-4938-4167
Sayfa
841–854
Yayın Tarihi
2026-07-20
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Test sonuçları, hem küçük hem de büyük veri setlerinde geleneksel yöntemlere kıyasla kayda değer performans artışlarını ortaya koymuştur.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Teknolojik gelişmeler, genomik verilerin toplanması, işlenmesi ve iletilmesini mümkün kılarken, bu verilerin korunması kritik bir zorluk oluşturmaktadır.
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Temel Bulgular & Veri:Test sonuçları, hem küçük hem de büyük veri setlerinde geleneksel yöntemlere kıyasla kayda değer performans artışlarını ortaya koymuştur.
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Sonuç & Katkı:Farklı saldırı türlerine karşı kapsamlı koruma sağlayan bu sistem, geniş çaplı genomik verilerin güvenliği için güçlü ve hızlı bir çözüm sunarak, transformer sinir ağlarının kriptografideki potansiyelini göstermektedir.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Genomik veri güvenliği · DNA şifreleme · yapay zekâ tabanlı kriptosistem

Özet

Teknolojik gelişmeler, genomik verilerin toplanması, işlenmesi ve iletilmesini mümkün kılarken, bu verilerin korunması kritik bir zorluk oluşturmaktadır. Mevcut şifreleme yöntemleri, yüksek işlem maliyetleri, karmaşık anahtar yönetimi ve sınırlı rastgelelik gibi nedenlerle geniş veri setlerinde yetersiz kalmaktadır. Bu çalışma, DNA hesaplamalarını ve transformer sinir ağı mimarisini birleştiren yenilikçi bir şifreleme sistemi önermektedir. Sistem, DNA hesaplamaları ve transformer sinir ağı mimarisi ile verileri şifrelerken, aynı zamanda güvenliği artırmak için dinamik bir sözde rastgele sayı üreteci (PRNG) geliştirmiştir. Üretilen DNA baz dizilimli dinamik PRNG, tüm NIST testlerini başarıyla geçerek güvenilir bir rastgelelik sunmuş, sinir ağı ise referans veriye bağımlı olmaksızın anahtar dizini oluşturmayı başarmıştır. Yöntemin, yüksek entropili DNA dizilerinin avantajları ve moleküler seviyede hesaplamaların paralel işlem kapasitesi sayesinde sofistike saldırılara karşı dayanıklı olduğu kanıtlanmıştır. Test sonuçları, hem küçük hem de büyük veri setlerinde geleneksel yöntemlere kıyasla kayda değer performans artışlarını ortaya koymuştur. Şifreleme ve çözme süreçlerinde sırasıyla 909 ve 1071 kat daha hızlı sonuçlar elde edilmiş, büyük veri setlerinde bu oranlar şifreleme için 923 kata, şifre çözme için ise 1714 kata kadar çıkmıştır. Farklı saldırı türlerine karşı kapsamlı koruma sağlayan bu sistem, geniş çaplı genomik verilerin güvenliği için güçlü ve hızlı bir çözüm sunarak, transformer sinir ağlarının kriptografideki potansiyelini göstermektedir.

Abstract

Although technological advances have made it possible to collect, process, and transmit genomic data, protecting this data poses a critical challenge. Existing encryption methods are inadequate for large datasets due to reasons such as high processing costs, complex key management, and limited randomness. This study proposes an innovative encryption system that combines DNA computations and a transforming neural network architecture. The system encrypts data with DNA computations and a transforming neural network architecture, while also developing a dynamic pseudo random number generator (PRNG) to increase security. The generated DNA base sequence dynamic PRNG successfully passed all NIST tests and provided reliable randomness, while the neural network was able to generate a key index without relying on reference data. The method has been proven to be resistant to sophisticated attacks thanks to the advantages of high-entropy DNA sequences and the parallel processing capacity of molecular-level computations. The test results revealed significant performance improvements compared to traditional methods on both small and large datasets. While 909 and 1071 times faster results were achieved in encryption and decryption operations, respectively, these rates increased up to 923 times for encryption and 1714 times for decryption in large datasets. Providing comprehensive protection against different types of attacks, this system offers a powerful and fast solution for the security of large-scale genomic data, demonstrating the potential of transformative neural networks in cryptography.

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Bu Makaleye Atıf Yap

Kaya, A., Türkoğlu, İ. (2026). Genomik verilerin güvenliği için transformer ağı destekli DNA şifreleme. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 41 Sayı: 2, 841–854. https://doi.org/10.17341/gazimmfd.1619337

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