Yazar "Elzaridi, Khalid Mohammed Abdullah" seçeneğine göre listele

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  • Küçük Resim
    Öğe
    Integration between network intrusion detection and machine learning techniques to optimizing network security
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2024) Elzaridi, Khalid Mohammed Abdullah; Kurnaz, Sefer
    In an increasingly linked world beset with cybersecurity risks, the necessity for powerful intrusion detection systems (IDS) is paramount. This thesis proposes a fresh approach to IDS development. using modern machine learning algorithms and feature selection techniques to boost detection accuracy and resistance. Drawing upon lessons from earlier research, we address fundamental flaws in existing IDS approaches. emphasis on scalability and susceptibility to advanced assaults. Our suggested hybrid model, incorporating Random Forest, Gradient Boosting Machines, and Neural Networks, obtains a remarkable accuracy rate of 96% in identifying network intrusions. Utilizing the Intrusion Detection Evaluation Dataset (CIC-IDS2017), Our trials illustrate the efficacy of the proposed technique in realworld circumstances. This research contributes to the evolution of cybersecurity techniques by delivering practical insights for strengthening the security and resilience of digital infrastructures.
  • Küçük Resim
    Öğe
    XGBoost algorithm for orecasting electricity consumption of Germany
    (Altınbaş Üniversitesi, 2023) Ibrahim, Abdullahi Abdu; Elzaridi, Khalid Mohammed Abdullah
    Stability requires energy demand prediction. We train and test 24-hour German load forecasting models. ENTSO-E Transparency Platform data covered European energy generation, transmission, and consumption. It uses German load data instead of PJM data for the eastern US, adds holidays and lag features to the XGB model, and benchmarks with a linear model and a random forest. Grid search CV refines the final XGB model. National load forecasting RMSE is 1740MW, which is suitable for the gradient boosting model. H-24 and H-48 lag is the most important for this job. Weekends and holidays help, but less. Regional holidays, average temperatures, and lag characteristics could improve the model (beyond H-48).