Bulut Bilişim Güvenliği İçin Kullanılan Makine Öğrenimi Yöntemleri Üzerine Bir Derleme

Abstract

Son zamanlarda bulut bilişimin farklı amaçlar için kullanımı artmaktadır. Bu durum bulut üzerindeki bilgilerin çoğalmasına sebep olmaktadır ve daha yüksek güvenlik gereksinimlerinin olduğunu göstermektedir. Güvenliği sağlamanın yollarından bir tanesi makine öğrenmesi yöntemlerinin bulut sistemlerine adapte edilmesidir. Geleneksel yöntemler saldırılardaki çeşitlilik nedeniyle istenilen düzeyde başarı sağlayamamaktadır. Makine öğrenimi yaklaşımları, verileri daha etkin bir şekilde ele aldıklarından daha duyarlı ve otomatikleştirilmiş güvenlik çözümleri sunabilmektedir. Bulut üzerindeki verilerin gizliliği, bütünlüğü, bulut kaynaklarının kullanılabilirliği ve bulut platformu üzerindeki kimlik doğrulama işlemleri için makine öğrenimi tabanlı sistemlerin kullanımı son zamanlarda oldukça popülerdir. Genellikle izinsiz giriş tespit sistemi olarak adlandırılan bu sistemler, bulut uygulamalarındaki bilgileri yetkisiz erişimlerden korumak için kapsamlı yaklaşımlar kullanmaktadır. Bu çalışmada bulut bilişim güvenliği ve bu alanda kullanılan makine öğrenmesi yaklaşımları üzerine bir sistematik literatür taraması yapılmıştır. Kullanılan makine öğrenimi yöntemleri ve değerlendirme kriterleri, kullanılan veri kümeleri ve çalışmaların sağladıkları bilgi güvenliği kavramları baz alınarak, literatürde etkisi olan çalışmalar ele alınmıştır. Bazıları hibrit bazıları bağımsız şekilde 23 farklı makine öğrenimi yöntemi ve 17 farklı değerlendirme ölçütünün kullanıldığı görülmüştür. Toplamda 11 farklı hazır veri kümesi ve sekiz çalışmada ise oluşturulmuş olan veri kümelerinin kullanıldığı görülmüştür. Son olarak çalışmalar gizlilik, bütünlük, erişilebilirlik ve kimlik denetimi olacak şekilde bilgi güvenliği kavramları açısından değerlendirilmiştir.

Keywords

• Bulut Bilişim Güvenliği
• Makine Öğrenimi
• Performans Ölçütleri
• Veri Kümeleri

A Review of Machine Learning Methods Used for Cloud Computing Security

Abstract

Recently, the use of cloud computing for different purposes has been increasing. This causes the proliferation of information on the cloud and indicates higher security requirements. One of the ways to ensure security is to adapt machine learning methods to cloud systems. Traditional methods cannot achieve the desired level of success due to the diversity in attacks. Machine learning approaches can offer more responsive and automated security solutions as they handle data more effectively. The use of machine learning-based systems for the confidentiality and integrity of data in the cloud, the availability of cloud resources, and authentication on the cloud platform have been very popular recently. These systems, often called intrusion detection systems, use comprehensive approaches to protect the information in cloud applications from attacks. In this study, a systematic literature review was conducted on cloud computing security and machine learning approaches used in this field. Based on the machine learning methods and evaluation criteria used, the datasets used and the information security concepts provided by the studies, the studies that have an impact on the literature are  discussed. It has been observed that 23 different machine learning methods and 17 different evaluation criteria are used, some of the hybrid and some independently. In total, 11 different ready-made datasets and the datasets created in eight studies were used. Finally, the studies were evaluated in terms of information security concepts such as confidentiality, integrity, availability, and authentication.

Keywords

• Cloud Computing Security
• Machine Learning
• Performance Criteria
• Datasets

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