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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Zahraa M. Algelal
University Of Kufa
Computer Science & IT Electrical & Electronics Engineering

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Botnet detection using ensemble classifiers of network flow Zahraa M. Algelal; Eman Abdulaziz Ghani Aldhaher; Dalia N. Abdul-Wadood; Radhwan Hussein Abdulzhraa Al-Sagheer
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.334 KB) | DOI: 10.11591/ijece.v10i3.pp2543-2550

Abstract

Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence.
Human face recognition methods based on principle component analysis (PCA), wavelet and support vector machine (SVM) : a comparative study Eman A. Gheni; Zahraa M. Algelal
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 2: November 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i2.pp991-999

Abstract

Human face Recognition systems are increasingly gaining more importance and can be utilized throughout many applications like video surveillance, Security, human-computer intelligent interaction, etc. this paper presents performance comparison between three feature extraction techniques for an automatic face recognition system. In the first step, we benefit from wavelet Transforms, principal component analysis (PCA) and combining Wavelet with PCA as feature extracting methods. After feature vectors generation, linear and nonlinear support vector machines (SVM) are usually used for implementing the classification or recognition step. These methods are compared on accuracy in an ORL database for face recognition applications including 400 images of 40 people.
Co-Authors Dalia N. Abdul-Wadood Eman A. Gheni Eman Abdulaziz Ghani Aldhaher Radhwan Hussein Abdulzhraa Al-Sagheer