Yaser Ghaderipour

Work place: Department of Computer Science, University of Tabriz, East Azerbaijan Province, Tabriz, Iran



Research Interests: Software Engineering, Software Development Process, Software Creation and Management


Yaser Ghaderipour is from Kurdistan Province, Iran. He is an M.Sc. graduate in Computer Science from the University of Tabriz, Tabriz, Iran. His main research topics are Data Science and Intrusion Detection. He is currently working as a software developer and tries to produce secure and reliable codes in the banking and payment industry. He is responsible for developing and maintaining Payment Switch projects for Behpardakht Mellat (Best Payment Service Provider in the Middle East). He likes to play traditional instruments, read psychology books, and go swimming in his free time.

Author Articles
A Flow-Based Technique to Detect Network Intrusions Using Support Vector Regression (SVR) over Some Distinguished Graph Features

By Yaser Ghaderipour Hamed Dinari

DOI: https://doi.org/10.5815/ijmsc.2020.04.01, Pub. Date: 8 Aug. 2020

Today unauthorized access to sensitive information and cybercrimes is rising because of increasing access to the Internet. Improvement in software and hardware technologies have made it possible to detect some attacks and anomalies effectively. In recent years, many researchers have considered flow-based approaches through machine learning algorithms and techniques to reveal anomalies. But, they have some serious defects. By way of illustration, they require a tremendous amount of data across a network to train and model network’s behaviors. This problem has been caused these methods to suffer from desirable performance in the learning phase. In this paper, a technique to disclose intrusions by Support Vector Regression (SVR) is suggested and assessed over a standard dataset. The main intension of this technique is pruning the remarkable portion of the dataset through mathematics concepts. Firstly, the input dataset is modeled as a Directed Graph (DG), then some well-known features are extracted in which these ones represent the nature of the dataset. Afterward, they are utilized to feed our model in the learning phase. The results indicate the satisfactory performance of the proposed technique in the learning phase and accuracy over the other ones.

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