Automated Infrastructure Cybersecurity Management Using Deep Neural Networks: A Network Intrusion Detection Case Study

في بيئات تكنولوجيا المعلومات الحديثة، تُعد إدارة البنية التحتية المؤتمتة (AIM) تحولًا جذريًا في كيفية توفير ومراقبة وصيانة مكونات البنية التحتية الحيوية، وذلك من خلال الاستفادة من تقنيات متقدمة مثل الذكاء الاصطناعي (AI)، وتعلم الآلة (ML)، وإنترنت الأشياء (IoT). ونظرًا للتعقيد المتزايد في أنظمة البنية التحتية الحديثة، فإن الحلول الذكية أصبحت ضرورية لمنع الأعطال وتحسين الكفاءة التشغيلية، إلا أن أتمتة البنية التحتية تواجه تحدياتها الخاصة. تقدم هذه الورقة إطار عمل يعتمد على الشبكات العصبية العميقة (DNN) لرصد أعطال البنية التحتية على نطاق واسع وإجراء الصيانة التنبؤية. كما تم تقييم نتائج الدراسة بشكل نقدي، ومعالجة مخاطر الإفراط في التعلّم (Overfitting)، واقتراح استراتيجيات نشر فعالة لتطبيق DNNs في الأنظمة الواقعية. وفي الختام، تُظهر الدراسة كيف يمكن للشبكات العصبية العميقة أن تُحدث تحولًا في إدارة البنية التحتية من نمط الاستجابة إلى نمط التنبؤ الاستباقي. 4o      This study presents a Deep Neural Network (DNN)-based framework for Automated Infrastructure Management (AIM), the main aim being to develop and evaluate a DNN-based framework for real-time intrusion detection in automated infrastructure systems. Leveraging the UNSW-NB15 dataset, the research addresses the limitations of traditional rule-based systems by employing advanced techniques such as SMOTE for handling data imbalance, batch normalization for training stability, and feature selection to optimize model performance. The developed DNN model achieved an accuracy of 99.61% and an AUC of 0.9993, demonstrating exceptional capability in classifying normal and attack traffic with high precision and recall. While the results highlight the potential of DNNs to revolutionize infrastructure management through predictive maintenance and real-time threat detection, challenges such as reliance on synthetic data, computational demands, and cross-domain generalizability remain. The study underscores the importance of integrating real-time data, developing lightweight models for edge deployment, and addressing ethical considerations to ensure scalable and trustworthy AIM solutions in diverse infrastructure environments

Combining Deep Learning with Edge Computing in Improving Accessibility and Performance of E-Learning

   Through a descriptive analysis, this study investigates how to improve the performance and accessibility of e-learning systems integrating deep learning (DL) with edge computing (EC). The COVID-19 epidemic has exposed obstacles to real-time interactions and scalability in traditional cloud-based e-learning, including latency, bandwidth limitations, and privacy problems. Through utilizing deep learning\u27s adaptability and edge computing\u27s decentralized design, this study suggests a three-tier architecture (end-user devices, edge servers, and cloud clusters) to enhance security, minimize latency, and optimize data processing. Through a comparative analysis of cloud-based and edge-enabled systems, the study highlights the advantages of this hybrid approach, including faster response times, reduced network congestion, and enhanced privacy. The findings demonstrate the potential of edge-based deep learning to revolutionize e-learning by enabling personalized, real-time, and offline-capable educational experiences

A Robust Hybrid Model Integrating GANs, XGBoost, and Reinforcement Learning (RL)

This study introduces a robust hybrid model that integrates Generative Adversarial Networks (GANs), eXtreme Gradient Boosting (XGBoost), and Reinforcement Learning (RL) to enhance predictive analysis and anomaly detection in financial data, specifically targeting fraud detection and trend forecasting. Leveraging the unique strengths of each component, GANs for generating high-quality synthetic data to address class imbalance, XGBoost for precise prediction models, and RL for dynamic decision-making based on evolving data patterns, this unified framework offers a novel and ambitious approach to financial security. We detail the design, implementation, and comprehensive evaluation of this model using real-world financial datasets, demonstrating significant improvements in accuracy and decision-making speed within complex economic contexts. The proposed methodology addresses critical challenges such as data imbalance, evolving fraud patterns, and the need for adaptive decision-making, providing a scalable and effective solution for enhanced financial security. Experimental results demonstrate that the hybrid model achieves superior performance compared to individual components, with XGBoost achieving % accuracy, RL demonstrating 93.2% accuracy with excellent adaptability, and GANs providing effective data augmentation with 90.35% recall for fraud detection

Leveraging Big Data in The Banking Sector: An Analysis of Challenges and Opportunities at the Central Bank of Aden

تناولت هذه الدراسة التحديات التي يواجهها المترجمون المتدربون في ترجمة النصوص العلمية والتقنية بين اللغتين العربية والإنجليزية باستخدام منهجية الأساليب المتعددة. تم إجراء استبيان على 46 متدربًا من ثلاث جامعات يمنية لجمع بيانات حول تجاربهم في الترجمة والتحديات التي يواجهونها. بعد ذلك، تم إجراء اختبار ترجمة مع 40 متدربًا للحصول على مجموعة من عينات الترجمة، تتضمن مقتطفات من النصوص العلمية والتقنية. تم تحديد إجمالي 724 خطأ من خلال تحليل الأخطاء لهذه الترجمات، منها 228 خطأ في الترجمات من الإنجليزية إلى العربية و496 خطأ في الترجمات من العربية إلى الإنجليزية. أشارت النتائج إلى أن الأخطاء المعجمية كانت الأكثر تكرارا، إلى جانب الأخطاء النحوية والإملائية. وتشير النتائج إلى أن العوامل اللغوية والثقافية والمعرفية تساهم في هذه التحديات. وكشف التحليل أن تعزيز المفردات والمهارات النحوية، إلى جانب التدريب الخاص بالوسائط، أمر بالغ الأهمية لتحسين دقة الترجمة. تؤكد الدراسة على الحاجة إلى برامج تدريبية متخصصة تتناول هذه المجالات المحددة، مؤكدة على أن الجهود المتضافرة من كل من مؤسسات التدريب والباحثين ضرورية لتعزيز كفاءات المتدربين المترجمين العاملين مع اللغتين العربية والإنجليزية.Central banks in developing countries face numerous challenges, including counterfeiting and decision-making difficulties. These banks generate vast amounts of data daily, which may not be fully utilized. This study aims to explore the scope, nature, characteristics, sources, and application areas of Big Data in central bank of Aden, with a focus on why central bank should adopt it. The researchers used a descriptive methodology, conducting a comprehensive review of relevant studies, scientific theses, and research papers. The collected data were analyzed using SPSS software. A questionnaire, which was validated by experts and academic staff from the University of Science and Technology, was administered to the information technology management staff at the Central Bank of Yemen\u27s headquarters in Aden and its branches. The study\u27s findings led to several recommendations and identified future research opportunities, emphasizing the study\u27s importance as a foundational effort in this area

Leveraging Big Data in The Banking Sector: An Analysis of Challenges and Opportunities at the Central Bank of Aden

Central banks in developing countries face numerous challenges, including counterfeiting and decision-making difficulties. These banks generate vast amounts of data daily, which may not be fully utilized. This study aims to explore the scope, nature, characteristics, sources, and application areas of Big Data in central bank of Aden, with a focus on why central bank should adopt it. The researchers used a descriptive methodology, conducting a comprehensive review of relevant studies, scientific theses, and research papers. The collected data were analyzed using SPSS software. A questionnaire, which was validated by experts and academic staff from the University of Science and Technology, was administered to the information technology management staff at the Central Bank of Yemen's headquarters in Aden and its branches. The study's findings led to several recommendations and identified future research opportunities, emphasizing the study's importance as a foundational effort in this area