Android Güvenlik Açıklarının Modellenmesi: İstatistiksel Dağılımlardan Analizler

Year 2024, Volume: 8 Issue: 2, 110 - 126, 30.12.2024

Kerem Gencer , Fatih Basciftci

https://doi.org/10.33461/uybisbbd.1524207

Abstract

Android işletim sistemi, multimedya özelliklerini destekleyen bir mobil işletim sistemidir. Android, ses, video, resim ve diğer multimedya içeriklerini oynatmak, kaydetmek, düzenlemek ve paylaşmak için çok çeşitli uygulamalar ve entegre özellikler sunar. Çoğu Android cihazda kamera, hoparlör, mikrofon ve diğer multimedya bileşenleri bulunur. Yazılım güvenliğinde, güvenlik açıkları genellikle yazılım geliştirme sırasında ortaya çıkan kritik endişelerdir. Bu güvenlik açıklarını sürümden sonra tahmin etmek, risk değerlendirmesi ve azaltma için önemlidir. Çeşitli modeller araştırılmış olsa da Android işletim sistemi nispeten keşfedilmemiş durumdadır. Bu çalışma, yaygın olarak kullanılan Alhazmi-Malaiya Lojistik (AML) modeline uygunluklarını karşılaştırarak, farklı istatistiksel dağılımlar kullanarak Android güvenlik açıklarını modellemeyi araştırmaktadır. 2016'dan 2018'e kadar uzanan Ulusal Güvenlik Açığı Veritabanı'ndan (NVD) alınan veriler ve Ortak Güvenlik Açığı Puanlama Sistemi (CVSS) puanları analiz edilmiştir. Çalışma, aylık güvenlik açığı sayıları ve ortalama aylık etki değerleri için Lojistik, Weibull, Nakagami, Gamma ve Log-lojistik dahil olmak üzere çeşitli dağıtım modellerini değerlendirir. Model sağlamlığı değerlendirmesi için uyum iyiliği testleri ve bilgi kriterleri uygulandı. Bulgular, araştırmacılar ve Android yazılım geliştiricileri için değerli içgörüler sunarak tahmin, risk değerlendirmesi, kaynak tahsisi ve araştırma yönüne yardımcı olur. Ortalama aylık etki değerleri ve aylık güvenlik açığı sayıları için sırasıyla lojistik ve Nakagami dağılımları en uygun modeller olarak ortaya çıkmıştır. Son olarak, istatistiksel yöntemler, anlaşılabilirlik, veri miktarı, hesaplama ihtiyacı ve veri bağımsızlığı gibi esnek özellikleri nedeniyle küçük veri kümeleri veya daha net tanımlanmış veriler için bilinen yapay zekâ yöntemlerine karşı daha iyi performans gösterir.

Keywords

İstatistiksel dağılımlar, Android güvenlik açıkları, Yazılım güvenliği, Güvenlik açığı keşif modeli

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Abstract

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

Keywords

Statistical distributions, Android vulnerabilities, Software security, Vulnerability discovery model

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