Apr, 2015
Master of Science in Computer Science

Computer Science

University of Zakho

Jul, 2009
B. Sc

Computer Science

University of Duhok

Oct, 2018
Assistant Lecturer

Reading books

Publication Journal
Apr, 2019
Combining Best Features Selection Using Three Classifiers in Intrusion Detection System

IEEE Journal of the American Institute of Electrical and Electronic Engineers (Issue: 978-1-5386-9343-8/19/$31.00 ©2019 IEEE) (Volume: page 94-99)

Nowadays, with the development of internet technologies service in the world, the intruders has been increased rapidly. Therefore, the advent of Intrusion Detection System (IDS) in the security of networks field prevents intruders from having access to the information. IDS plays an important role of detecting different types of attacks. Because network traffic dataset has many features, the process of feature selection and removing irrelevant features increase the performance of the classification algorithms accuracy. This paper provides three various methods which are: Firstly, Information Gain. Secondly, Gain Ratio. Thirdly, Correlation Feature Selection. These techniques are used for selecting and ranking features then select combine the best top ranking features. Only six features were selected out of 41 features. These features are tested on three classifiers (K-Nearest Neighbor, Naïve Bays and Neural Network based Multilayer Perceptron) for classification and detect intrusion. The outcome illustrates that a high level of attacks classification accuracy can be accomplished by combing best different features selection. Moreover, K-Nearest Neighbor gets high accuracy classification for IDS. The proposed model has been applied on KDD data set and ten cross validation process used to assess the classification performance.

Jan, 2015
Combination of Multi Classification Algorithms for Intrusion Detection System

International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 (Issue: 1) (Volume: 6)

Classification is one of the common tasks that are involved in data mining to build models for the prediction of future data. It performs its task by different classifier algorithms. This paper provides an approach based on information gain to determine the most distinguishing subset features of each attack class and combine multi classification algorithms which includes (Decision Tree J48, k nearest Neighbor and Naïve Bays). These classifiers are used for the task of detecting intrusions and comparing their relative performances. The goal of this work is to analyze the performance and accuracy of classification algorithms in order to identify the most efficient algorithm for each attack class, and then build accurate intrusion detection system. The proposed model has been applied on KDD Cup 99 data set using 60% of them for training and 40% for testing. These experimental results show that multiple classifiers work better than a single classifier. Also, multiple classifiers are more accurate and have abilities of distinguishing among the different attacks and normal connections effectively.

Apr, 2019 - Apr, 2019
2nd 2019 International Conference on Advanced Science and Engineering (ICOASE), University of Zakho, Duhok Polytechnic University

Kurdistan Region - Iraq, Duhok Polytechnic University , University of Zakho As Presenter

2nd 2019 International Conference on Advanced Science and Engineering (ICOASE), University of Zakho, Duhok Polytechnic University The Conference is the premier forum for presenting the new results of advanced topics in science, engineering, and their applications

Jul, 2019 - Jul, 2019
Workshop of OPATEL project , Workshop on Learning Management Systems

Leipzig University of Applied Science (HTWK) , in Germany As Presenter

Project (OPATEL ) is Online Platform for Academic TEaching and Learning in Iraq and Iran The aim of OPATEL consists in establishment and development of E-Learning center to train staff and students in Iraqis and Iranian universities by using online platform for teaching and learning. the workshop about electronic learning in university.


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