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Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of network topology have been generated to observe the effectiveness of proposed algorithms on different network architectures. The results reveal that RF performs better than KNN in a single topology, and both have close performance in other topologies.

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Publication Date
Thu May 02 2019
Journal Name
Journal Of Planner And Development
The Developmental Role of Ajloun Forest Reserve in Jordan
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Ajloun reserve is the most important reserved areas set up by the Jordanian government to encourage development and ecotourism, is located on the green mountains of Ajloun north of Jordan, was founded in 1989 and follow The Royal Society for the conservation of the natur, an area of 13 km2, and covered with thick forests of oak trees and Butm, maple and others, characterized by the diversity of vital consists of 319 species of medicinal and aromatic plants and wild flowers, and where there are 40 species of birds, 14 species of mammals, 16 reptiles, 5 species of plants registered in the world. The Reserve has a great importance in Jordan ,it is the most reservations attractive to tourists, both internally and externally, and prov

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Publication Date
Mon Sep 01 2025
Journal Name
Microbial Biosystems
Harnessing cyanobacteria for a greener tomorrow: CO₂ mitigation and bioconversion to sustainable chemicals and fuels
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Publication Date
Thu Aug 01 2024
Journal Name
Advances In Science And Technology Research Journal
Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
A new smart approach of an efficient energy consumption management by using a machine-learning technique
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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Communications
SDN Implementation in Data Center Network
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Publication Date
Sun Sep 03 2017
Journal Name
Baghdad Science Journal
Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor
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There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Al-farabi For Engineering Sciences Vol
Prototyping of Multi-Factors Based Vehicle Accident Detection and Reporting System Relying on GPS and GSM
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Publication Date
Sat Sep 26 2026
Journal Name
Journal Of Studies And Researches Of Sport Education
Design and rationing test compound to assess the performance of the attack straight in terms of power characteristic speed And vital capacity for young players duel weapon Blinds
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Publication Date
Sat Nov 01 2025
Journal Name
Iop Conference Series: Earth And Environmental Science
Analysis the Seismic Activity in Iraq: Impacts on Foundation Selection and Mitigation Strategies
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Abstract<p>Recently, Iraq has experienced more earthquakes, notice that its increased about 62% in 2024 in comparison with same period of 2023, the main aims of this study are to detect the causes and contributing factors of the earthquakes in Iraq. Natural disasters like earthquakes contributes to about 61 %. Also, they are responsible for 94% of all human deaths from natural disasters. The fundamental reasons of earthquakes occur in Iraq due to geological location, which lies on the boundary of the Arabian plate and the Iranian-Eurasian plate. There is much seismic activity in Iraq’s east and north because of the collision of the above plates. Another cause of earthquakes are faults and f</p> ... Show More
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Publication Date
Wed Mar 20 2024
Journal Name
Journal Of Petroleum Research And Studies
Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field
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Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

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