abnormal circumstances while monitoring the systems and target to take the necessary precaution. Attack detection mechanisms used by IDS are basically divided into two. These are signature-based methods and anomaly based methods. Signature-based methods are used more for known intrusion types prevalently. Anomaly based methods model the normal behavior of the network and determines abnormal circumstances through the obtained model of normal state. Anomaly based methods are preferred more as they are more successful than the signature-based model in terms of detecting the new intrusion types. Anomaly detection systems determine behaviors that show deviation from the expected normal usage profiles as an anomaly. In the detection of the anomaly the normal behavior of the system is generally obtained using the statistical methods. One of the statistical methods used generally for this purpose is the Hidden Markov Model (HMM). Markov model is used to represent the tiered observations happen in time. HMM is a special version of the Markov

Creation of data sets to be used for studies in many different fields of research is really important process. However these data sets suffer from the problem of missing values. There are many different ways of handling missing values. Deletion methods and single imputation methods are the most common ones of these methods. However, this methods lead to high errors in data sets with high loss rates. Data sets used for the analysis of network traffic are also commonly encounters with the missing values. In this study, data produced in different sizes and different missing value rates for the analysis of network traffic in distributed systems. Then, different data imputation methods are compared for dealing with missing values in these datasets. Experimental results showed that Expectation Maximization Method is more applicable and performs better at relatively high missing data rates and k Nearest Neighbors Method performs better at low missing rates.

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