http://uproonline.com/index.php/UJCT

Impact Factor 0.875, Universal Journal of Computers & Technology (UJCT) is an International, open access, peer reviewed, online journal. The journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific preeminence in all areas of theoretical, experimental and applied UJCT.

http://uproonline.com/index.php/UJCT

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Submission Method

Method: 1 (Recommended): You may easily submit paper via UJCT online submission by using this link: Register. Method: 2 (Alternate): You can directly mail your paper at ujct@uproonline.com, editorujct@gmail.com. http://uproonline.com/index.php/UJCT
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UJCT

  UNIVERSAL JOURNAL OF COMPUTERS & TECHNOLOGY (UJCT) ISSN: 2456-2955 IMPACT FACTOR: 0.875 ,   Indexed with  Google Scholar , Scribd,  Research Bib ,  Scientific Indexing Services (SIS) , Directory of Research Journals  Indexing, Research Gate, Cosmos, Root Indexing, Citefactor ESJI Index Journal, Science Library Index & many more. You can directly mail your paper at   ujct@uproonline.com ,  editor ujct@gmail.com .  Website: http://uproonlin . ecom/index.ph p/UJCT Email Id: ujct@uproonline.com ,  editorujc t@gmail.com Register Online: http://uproonline.com/index.ph p/UJCT/user/register Paper template can be downloaded from  this link The advantages to you of publishing in UJCT •       Full open access: everyone can read your article when it is published •       Prompt and fair peer review from two or more expert peer r...
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Automatic Intelligent Real-time Intrusion Detection System: A Data Mining Model

Automatic Intelligent Real-time Intrusion Detection System: A Data Mining Model Oriola Oluwafemi Abstract Intrusion Detection System (IDS) is a popular device for securing network resources. However, developing a system that will automatically detect all known and unknown network attacks accurately in real-time is still a serious challenge. In this paper, a data mining model of an automatic intelligent real time network intrusion detection system that will be able to detect malicious network packets with very minimal false positive and false negative was presented. A three-layered Intrusion Detection System was modelled using Clustering techniques, Artificial Neural Networks and Decision Tree Classifiers. The algorithms used for each stage of the process were compared. Simple K-Means and Expectation Maximization Algorithms are compared in Layer One. In Layer Two, Multilayer Perceptron and Radial Basis Function Feed-Forward Back Propagation Neural Networks with varying para...
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