Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal International Journal of Advances in Intelligent Informatics
Majd Latah
Ege University - Izmir - Turkey.
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Human action recognition using support vector machines and 3D convolutional neural networks Majd Latah
International Journal of Advances in Intelligent Informatics Vol 3, No 1 (2017): March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i1.89

Abstract

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.
A novel intelligent approach for detecting DoS flooding attacks in software-defined networks Majd Latah; Levent Toker
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i1.138

Abstract

Software-Defined Networking (SDN) is an emerging networking paradigm that provides an advanced programming capability and moves the control functionality to a centralized controller. This paper proposes a two-stage novel intelligent approach that takes advantage of the SDN approach to detect Denial of Service (DoS) flooding attacks based on calculation of packet rate as the first step and followed by Support Vector Machine (SVM) classification as the second step. Flow concept is an essential idea in OpenFlow protocol, which represents a common interface between an SDN switch and an SDN controller. Therefore, our system calculates the packet rate of each flow based on flow statistics obtained by SDN controller. Once the packet rate exceeds a predefined threshold, the system will activate the packet inspection unit, which, in turn, will use the (SVM) algorithm to classify the previously collected packets. The experimental results showed that our system was able to detect DoS flooding attacks with 96.25% accuracy and 0.26% false alarm rate.
Co-Authors Levent Toker