Sunucuların Anomali Durumlarinin Yapay Zeka Metotları ile Tahmin Edilmesi

Year 2023, Volume: Vol:8 Issue: Issue:2, 57 - 65, 20.12.2023

Mehmet Fatih Savran Ahmet Anıl Müngen

https://doi.org/10.53070/bbd.1223978

Abstract

Sunucularda anormallik tespiti belirli bir metot ve uygulama yoluyla aykırı değerlerin diğerlerinden ayrıştırılarak analiz edilmesiyle oluşan ve genelde olağan dışı durumları tespit etmekte kullanılır. Anormalliklerin erkenden tespit edilmesi ön görülebilirlik kararlar vermeyi ve gerekirse savunma mekanizması geliştirilmesinde kullanılabilir. Önemli bir problem olarak bilinen Anormallik Tespiti birçok tarama ve uygulama sahasında araştırılmaktadır. Genelde araştırmacılar bu bahsi geçen probleme yapay zeka, makine öğrenimi ve durum makine modellemesi gibi teknikleri kullanarak çözüm arayışına girmişlerdir. Sunucuların anormallik testleri ve analizi yapılabilir ve bu yöntem-teknikler kullanılarak çıkarımlar yapılabilir. Sunuculardan alınan CPU, Network, Disk, Memory değerleri anomali testinde kullanılmak üzere veri analiz aşamalarından geçer ve teknikler uygulanarak modellemesi yapılır. Bu çalışmada toplanan veri kümesi kullanılarak YSA, Karar Ağacı, Rastgele Orman, K- En Yakın Komşu ve Ekstra Karar Ağacı algoritmalarının anormali tespit performansları test edilmiştir. Yapılan testlerde anormal durumlarının belirlenmesinde % 99.94 oranıyla YSA’nın başarılı olduğu görülmüştür. Önerilen yöntem, toplanan veri ve önerilen yöntemin diğer yöntemler ile karşılaştırmalı analizleri çalışma içerisinde sunulmuştur.

Keywords

Anormallik Tespiti, Yapay Zeka, Sunucular, Bulut Sistemler

Prediction of Anomaly Conditions of Servers with Artificial Intelligence Methods

Abstract

The objective of this paper is to investigate the efficacy of various techniques in detecting anomalies in server systems. Anomaly detection is a critical problem that involves identifying unusual situations by analyzing outliers and making predictions based on these observations. The study focuses on using artificial intelligence, machine learning, and state machine modeling to solve this problem. The data collected from the servers, including CPU, network, disk, and memory values, is analyzed and used for anomaly testing and modeling. The performance of five algorithms, including Artificial Neural Network (ANN), Decision Tree, Random Forest, K-Nearest Neighbor, and Extra Decision Tree, is evaluated using the collected data set. The results show that the ANN algorithm achieved a success rate of 99.94% in detecting abnormal conditions. The study presents a comprehensive analysis of the proposed method, the collected data, and a comparison of the proposed method with other methods. The findings of this study contribute to the ongoing efforts to improve the accuracy and efficiency of anomaly detection in server systems.

Keywords

Anomaly Detection, Artificial Intelligence, Servers, Cloud System

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