Bir Sözde Rassal Sayı Üreticinin Parçacık Sürü Optimizasyonu Yöntemi Kullanılarak Gerçek Zamanlı Optimizasyonu

Yıl 2022, Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 80 - 85, 10.10.2022

Muhammed Saadetdin Kaya Kenan İnce

https://doi.org/10.53070/bbd.1173694

Öz

Sistem tasarımı ve kriptografik yöntemler için kritik bir öneme sahip olan rassal sayı üretimi; işlem gücü yüksek bilgisayarların ortaya çıkmasıyla güvenlik açısından daha da ön plana çıkmaktadır. Bu problemin çözülmesi için fiziksel bir işleyiş ile rassal sayı üretimini hedefleyen gerçek rassal sayı üreteçleri kullanılabileceği gibi yazılım tabanlı olduğu için uygulanması daha kolay olan sözde rassal sayı üreteçleri (SRSÜ) de kullanılabilmektedir. SRSÜ, genellikle bilinen bir algoritmaya sahip olmaları ve aynı şartlar altında tahmin edilebilen sonuçlar vermeleri sebebiyle gerçek manada rassallık sağlayamamaktadırlar. Nitekim çeşitli rassallık şartlarını sağlamaları, sayı üretim hızı ve maliyet gibi sebeplerden dolayı sıkça tercih edilmektedirler. Bu çalışmada, uygulama kolaylığı ve uygulama ortamı sebebiyle tercih edilen bir SRSÜ algoritmasının Parçacık Sürüsü Optimizasyonu (PSO) kullanılarak değişken sistem şartlarında asgari kaynak tüketimi ile azami rassallığa ulaştırılması amaçlanmıştır. Rassallık, Tekrarlama Sınaması ve Sıfır Hipotezi kullanılarak ölçülmüş ve PSO kullanılarak bir SRSÜ’nün optimize edilmesi yoluyla özellikle alan karmaşıklığı açısından ciddi kazanımlar elde edilebileceği sonucuna ulaşılmıştır.

Anahtar Kelimeler

Sözde Rassal Sayı Üreteci (SRSÜ), Parçacık Sürü Optimizasyonu (PSO), Koşu Testi, Kriptografi

Destekleyen Kurum

İnönü Üniversitesi

Proje Numarası

FBG-2020-2143

Teşekkür

Bu çalışma, İnönü Üniversitesi Bilimsel Araştırma Projeleri Bölümü'nün (BAPB) FBG-2020-2143 sayılı projesi ile desteklenmiştir. Yazar, değerli geri bildirimleri için İnönü Üniversitesi BAPB’ye teşekkür eder.

Real-Time Optimization of a Pseudo-Random Number Generator Using Particle Swarm Optimization Method

Öz

Random number generation, which has a critical importance for system design and cryptographic methods; with the emergence of computers with high processing power, security comes to the fore even more. In order to solve this problem, real random number generators that aim to generate random numbers with a physical operation can be used, as well as pseudo-random number generators (PRNG), which are easier to implement because they are software-based. PRNG cannot provide real randomness because they generally have a known algorithm and give predictable results under the same conditions. As a matter of fact, they are frequently preferred because they provide various randomness conditions, number generation speed and cost. In this study, it is aimed to achieve maximum randomness with minimum resource consumption under variable system conditions by using Particle Swarm Optimization (PSO) of a PRNG algorithm, which is preferred due to its ease of implementation and application environment. Randomness was measured using the Runs Test and the Null Hypothesis, and it was concluded that significant gains can be achieved, especially in terms of space complexity, by optimizing a PRNG using PSO.

Anahtar Kelimeler

Pseudo-random Number Generator (PRNG), Particle Swarm Optimization (PSO), Runs Test, Cryptography

Proje Numarası

FBG-2020-2143

Kaynakça

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