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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
Sun Jul 02 2017
Journal Name
Journal Of Educational And Psychological Researches
Prevalence of Posttraumatic Stress Symptomatology and Distribution of Attachment Patterns 1 and 5 Months after a Terrorist Attack
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As a consequence of a terrorist attack, people may experience posttraumatic stress disorder (PTSD) and lack of feeling secure in relationships. This longitudinal study aimed to examine the prevalence of PTSD symptoms over time, the relationship between adult attachment styles and PTSD, as well as their association with degree of exposure, and finally to consider the distribution and the trajectory of attachment styles. The sample consisted of 235 students (M=125, F=110) who were exposed to different levels of trauma intensity in response to a bombing attack. Participants were recruited and assessed approximately 1 month and 5 months after the attack using a battery of questionnaires. Findings revealed, as expected, that 79.5% of the part

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Data And Network Science
The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms
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This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big

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Publication Date
Sun Jan 01 2023
Journal Name
E3s Web Of Conferences
Mitigation of Reflection Cracking in Asphalt Concrete Overlay on Rigid Pavements
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Reflective cracking is one of the primary forms of deterioration in pavements. It is widespread when Asphalt concrete (AC) overlays are built over a rigid pavement with discontinuities on its surface. Thus, this research work aims to reduce reflection cracks in asphalt concrete overlay on the rigid pavement. Asphalt Concrete (AC) slab specimens were prepared in three thicknesses (4, 5, and 6 cm). All these specimens were by testing machine designed and manufactured at the Engineering Consulting Office of the University of Baghdad to examine for the number of cycles and loads needed to propagate the reflection cracking in the asphalt concert mixture at three temperatures (20, 30, and 30°C). It was noticed that the higher thickness A

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Publication Date
Wed Sep 01 2021
Journal Name
Journal Of Engineering
Spike neural network as a controller in SDN network
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The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.

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Publication Date
Thu Apr 06 2023
Journal Name
International Journal Of Emerging Technologies In Learning (ijet)
The Impact of a Scenario-Based Learning Model in Mathematics Achievement and Mental Motivation for High School Students
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Publication Date
Wed Jun 26 2019
Journal Name
Journal Of Mechanics Of Continua And Mathematical Sciences
VSM Based Models and Integration of Exact and Fuzzy Similarity For Improving Detection of External Textual Plagiarism admin June 29, 2019
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Publication Date
Tue Mar 21 2023
Journal Name
Biomedical And Pharmacology Journal
Development and Validation of HPLC Method For the Detection of Fusidic Acid Loaded in Non-ionic and Cationic Nanoemulsion-Based Gels
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Fusidic acid (FA) is a well-known pharmaceutical antibiotic used to treat dermal infections. This experiment aimed for developing a standardized HPLC protocol to determine the accurate concentration of fusidic acid in both non-ionic and cationic nano-emulsion based gels. For this purpose, a simple, precise, accurate approach was developed. A column with reversed-phase C18 (250 mm x 4.6 mm ID x 5 m) was utilized for the separation process. The main constituents of the HPLC mobile phase were composed of water: acetonitrile (1: 4); adjusted at pH 3.3. The flow rate was 1.0 mL/minute. The optimized wavelength was selected at 235 nm. This approach achieved strong linearity for alcoholic solutions of FA when loaded at a serial concentrati

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
On Existence of Prime K-Tuples Conjecture for Positive Proportion of Admissible K-Tuples
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Number theorists believe that primes play a central role in Number theory and that solving problems related to primes could lead to the resolution of many other unsolved conjectures, including the prime k-tuples conjecture. This paper aims to demonstrate the existence of this conjecture for admissible k-tuples in a positive proportion. The authors achieved this by refining the methods of “Goldston, Pintz and Yildirim” and “James Maynard” for studying bounded gaps between primes and prime k-tuples. These refinements enabled to overcome the previous limitations and restrictions and to show that for a positive proportion of admissible k-tuples, there is the existence of the prime k-tuples conjecture holding for each “k”. The sig

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Publication Date
Mon Feb 10 2025
Journal Name
Journal Of Optics
Implementing quantum key distribution based on coincidence detection captured from two different single photon detection modules
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Quantum key distribution (QKD) provides unconditional security in theory. However, practical QKD systems face challenges in maximizing the secure key rate and extending transmission distances. In this paper, we introduce a comparative study of the BB84 protocol using coincidence detection with two different quantum channels: a free space and underwater quantum channels. A simulated seawater was used as an example for underwater quantum channel. Different single photon detection modules were used on Bob’s side to capture the coincidence counts. Results showed that increasing the mean photon number generally leads to a higher rate of coincidence detection and therefore higher possibility of increasing the secure key rate. The secure key rat

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Publication Date
Fri Jan 13 2023
Journal Name
Atmosphere
Impact of North African Sand and Dust Storms on the Middle East Using Iraq as an Example: Causes, Sources, and Mitigation
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This study aims to determine the reasons for the increase in the frequency of sand and dust storms in the Middle East and to identify their sources and mitigate them. A set of climatic data from 60 years (1960–2022) was analyzed. Sand storms in Iraq are a silty sand mature arkose composed of 72.7% sand, 25.1% silt, and 2.19% clay; the clay fraction in dust storms constitutes 70%, with a small amount of silt (20.6%) and sand (9.4%). Dust and sand storms (%) are composed of quartz (49.2, 67.1), feldspar (4.9, 20.9), calcite (38, 5), gypsum (4.8, 0.4), dolomite (0.8, 1.0), and heavy minerals (3.2, 6.6). Increasing temperatures in Iraq, by an average of 2 °C for sixty years, have contributed to an increase in the number of dust storm

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