Imam Abdulrahman Bin Faisal University
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/16
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Item Restricted Machine Learning Based Models for Cyber-Attribution(Imam Abdulrahman Bin Faisal University, 2022) Alkaabi, Maimonah Rashed Hamad; Olatunji, Sunday36 0Item Restricted Machine Learning-Based Model for Exploring the Seasonal and Temporal Impact on the Psychological States of Twitter Users in Saudi Arabia(Imam Abdulrahman Bin Faisal University, 2022) Al Sari, Bador Ali Saad; Aslam, Nida34 0Item Restricted Tweet’s Bot Detection Using Machine Learning and Deep Learning with Feature Extraction(Imam Abdulrahman Bin Faisal University, 2022) Al-Dossary, Arwa Rashed; Abdur Rab, Irfan Ullah13 0Item Restricted Machine Learning Based Modeling For Effective And Accurate Prediction Of Solar Radiationin Saudi Arabia(Imam Abdulrahman Bin Faisal University, 2019) Syed, Hajra Fahim; Olatunji, Sunday OlusanyaWith the increase in population size, the demand for energy is expected to rise drastically. As a result, more resources will be needed to be utilized to meet increased electricity production demands. Therefore, the use of renewable energy resources like solar energy captured by photovoltaic (PV) panels is being considered as an alternative to non-renewable resources. However, solar radiation is subjected to changing weather conditions which introduces uncertainty in the amount of electricity that can be generated. To overcome this uncertainty, this thesis compares the performance of Decision Tree (DT), Artificial Neural Network (ANN), and ensemble methods including Random Forest (RF) and Gradient Boosting (GB) in predicting the Global Solar Radiation (GSR) of the next 30 minutes intervals. National Renewable Energy Laboratory (NREL) solar radiation dataset of Jeddah and Qassim cities was used. A comprehensive literature review of related studies on solar radiation prediction was performed. In addition, two data partitions methods for the 5-year data were used; 4 years for training and 1 year for testing, and a random split of 70% for training and 30% for testing, and their performance was compared. The parameters of DT, ANN, RF, and GB were tuned to get the optimized performance. In addition, correlation-based feature elimination was used for feature selection. Moreover, an analysis of how well the proposed solar radiation prediction models generalize to other locations was performed. Finally, from the experiments it was found that the ensemble models (RF and GB) performed better than the single machine learning models (ANN and DT).15 0Item Restricted Sentiment Analysis Of Arabic Tweets Based On Ensemble Machine Learning Approach(Imam Abdulrahman Bin Faisal University, 2019) Alyami, Sarah Nassir Saleh; اولوتانجي، سانديSentiment analysis is a powerful technique used to analyze and classify opinions and emotions expressed in textual writings. The increased use of social networks in the Arab world has provided a rich research ground for sentiment analysis. In this thesis, the problem of sentiment classification of Arabic text was addressed. A model based on machine learning was proposed to classify the underlying sentiments as being positive or negative. The proposed model is a voting ensemble of three classifiers: Support Vector Machine, AdaBoost and Naive Bayes Multinomial. To evaluate the effectiveness of the proposed model, a dataset was created from tweets discussing several polarizing social events associated with Saudi Arabia’s vision 2030. The dataset was manually annotated according to sentiment. In addition to the created dataset, an earlier dataset was used in this thesis as a baseline dataset. The proposed sentiment analysis framework involved several linguistic techniques, including light stemming and word normalization. Also, multiple feature sets were extracted and explored such as N-grams, tweet topic and emoticons-based features. Furthermore, the obtained results were optimized by finding the optimal classifier parameters, investigating several ensemble combination rules and applying correlation-based recursive feature elimination. The experimental results revealed excellent classification accuracy and portrayed the ensemble’s ability to perform better than the performance of its base classifiers. Moreover, the results indicated that the proposed ensemble technique outperformed the earlier used model on the same baseline dataset by an 8.5% accuracy increase, which is an indication of the superiority of the proposed model.17 0Item Restricted Attack and Defense Modeling Against DDoS Attacks in Industrial SCADA System Using Machine Learning(Saudi Digital Library) Aldahasi, Ezaz Mohammed SaadSCADA systems are monitoring and controlling critical infrastructures such as power generation, water supply, transportation networks, and manufacturing facilities. In order to ensure those critical infrastructures protection, it is necessary to understand all the ways in which the systems can be attacked and adopted a viewpoint similar to an attacker for determining weaknesses and hiatus in defenses. In fact, SCADA systems are very attractive targets for many malicious attacks. Many factors contributed to the increased risk of modern control systems, including network infrastructure, poor configurations, lack of skilled people, and reliance on standard technologies with a number of known vulnerabilities that are exploited by the attackers. The aim of this thesis is to investigate malicious attacks affecting the security of SCADA systems. The main focus is made on the DoS and DDoS attacks which have become a real threat to the functions of SCADA systems in a normal way. We have developed an attack tree for SCADA systems providing the possible routes for the attackers to disrupt the systems. We have contributed in developing attack and defence modelling for SCADA systems. We have worked on detecting DoS and DDoS attacks on SCADA systems and provided recommendations to avoid those harmful attacks. We have used the Honeypot approach to detect DoS and DDoS attacks on SCADA systems. In addition, some machine learning techniques have been applied to find alternatives for detecting such type of attacks The proposed model proves to be good as our experimental results produce an accuracy of 99.9112 0