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Articles by Adil M. Salman
Total Records ( 2 ) for Adil M. Salman
  Adil M. Salman and Safaa O. Al-mamory
  Intrusion Detection Systems (IDSs) rely on feature selection algorithms when selecting the most important features; this has an effect on both the accuracy and the time it takes to classify data. Several of these algorithms deal with a number of classes to classify the data. In this study we will evaluate several methods relating to feature selection which utilise adifferent number of classes of the classification in order to determine the optimal number of classes that deliver the best results basedon two criteria the overall accuracy and the time it takes to completethe classification.We utilised WEKA 3.8.0 software for datamining as well as to analyse two types of datasets which are KDD-CUP and NSL-KDD the datasets are each divided into three types based on (23, 5 and 2) classes. The reason behind choosing these numbers of classes is due to the fact that these datasets are available to the researchers on the internetat no cost.It was observed that through minimising the number of classes in classification algorithms, theresults arehighly accurate while training requires only ashort period of time; moreover, there are fewer selected features therefore the processing time is shorter.
  Adil M. Salman and Safaa O. Al-mamory
  In network security there is an essential field called intrusion detection system it is a method for detecting abnormal activities in network traffic. Another significant field in these systems is the feature selection methods which reduces the calculation time and tested data. This study introduces an evaluation of the most important features that used in intrusion detection methods of network flow to help the researchers knowing which features are important. Fifty-three different methods are investigated of feature selection and some intrusion detection methods including 39 methods that using different DARPA datasets and 14 methods using other different datasets. We also applied an experiment consists of 96 tests using WEKA 3.8.0 Software for datamining where we utilized 12 combinations of feature selection algorithms, the used datasets were KDD-CUP99 and NSL-KDD datasets. The contribution of this study is the focus on which of the features have the highest selected percentage for both the studied papers and our experiment. We have concluded that the basic features and the features based on the hosts which give the resource of the attacks was the most features that researchers used.
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