BT Sistemlerinde Veri Madenciliği Yöntemlerini Kullanarak Anomali Algılama: Karar Destek Uygulaması

Year 2018, , 1109 - 1123, 01.08.2018

Ferdi Sönmez , Metin Zontul Oğuz Kaynar , Hayati Tutar

https://doi.org/10.16984/saufenbilder.365931

Cited By: 2

Abstract

 Anomali tespiti üzerine çeşitli çalışmalar olmasına
rağmen, büyük ölçekli ağ ortamları için uygun yaklaşımların eksikliği nedeniyle
basit ve etkili anormali tespit yaklaşımları hala gereklidir. Mevcut
çalışmalardaki analiz yöntemlerinde, genellikle ön tanımlı analiz tekniklerinin
kullanıldığı, kapsam dışı durum ve olasılıkların dikkate alınmadığı ve
danışmansız öğrenen yapay sinir ağları (NN) metotlarının yeterince
kullanılmadığı görülmektedir. Alternatif olarak bu çalışmada özörgütlemeli
harita ağları kullanımı tercih edilmiştir. Diğer çalışmalarda, genellikle ağ
trafiğinden elde dilen veriler analiz edilirken, bu çalışmada diğer bilişim
sistemi verilerinin de analizine ve alternatif olabilecek çözüm önerilerine yer
verilmektedir. Ayrıca, büyük ölçekli ağ ortamlarında analiz edilecek verilerin
büyük boyutlu olmasına bağlı olarak, analiz çalışmalarında işlemlerin daha
hızlı gerçekleştirilebilmesi amacıyla uygulamada Bellek İçi Veritabanı
Sistemleri (BİVTS) kullanılmıştır. Bilişim sitemlerindeki yönetim araçlarından
edinilen uygulama log verilerinin analizi sonrasında anomali tespitinin %96
oranında başarı ile gerçekleştiği gözlenmiştir. Çalışmanın işletme ve
kullanıcılarına bilişim sistemlerini izleme ve güvenlik takibi işlemlerinde ön
tanımlama ihtiyacını ortadan kaldırarak, yoğun iş yükünü azaltma yönünde fayda
sağlayacağı düşünülmektedir. Böylelikle, önemli bir maliyet kaleminin ortadan
kalkacağı da düşünülmektedir. Ayrıca, öngörülemeyen hususlara bağlı güvenlik
açıkları ve problemlerin, uygulama ile tespiti ve böylelikle birçok saldırı ve
problemin önceden engellenileceği öngörülmektedir.

Keywords

Veri Analizi, Anomali Tespiti, Yapay Sinir Ağları, SOM, Bellek İçi Veritabanı Sistemleri

Anomaly Detection Using Data Mining Methods in IT Systems: A Decision Support Application

Abstract

Although there are various
studies on anomaly detection, simple and effective anomaly detection approaches
are still necessary due to the lack of appropriate approaches for large-scale
network environments. In the existing analysis methods, it is seen that the
methods of preliminary analysis are generally used, the extrapolations and probabilities
are not taken into account and the unsupervised neural network (NN) methods are
not used enough. As an alternative, the use of the Self-Organizing Maps has
been preferred in the study. In other studies, analysis of data obtained from
network traffic is analyzed, here, analysis of other information systems data
and suggestions for alternative solutions are given, too. In addition,
in-memory database systems have been used in practice in order to enable faster
processing in analysis studies, due to the large size of data to be analyzed in
large-scale network environments. An analysis of the application log data
obtained from the management tools in the information systems was carried out.
After anomaly detection results obtained and the verification test results are
compared, it is found out that anomaly detection process is successful by 96%.
The advantage offered for the company and users at IT and security monitoring
processes is to eliminate the need for pre-qualification and to reduce the heavy
workload. By this way, it is thought that a significant cost item is
eliminated. It is also contemplated that the security vulnerabilities and
problems associated with unpredictable issues will be detected through practice
and thus many attacks and problems will be prevented in advance.

Keywords

Data Analysis, Anomaly Detection, Artificial Neural Networks, Self-Organizing Maps, In-Memory Database Systems

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