{"id":23073,"date":"2026-06-24T16:59:30","date_gmt":"2026-06-24T13:59:30","guid":{"rendered":"https:\/\/su.edu.ye\/voga\/?p=23073"},"modified":"2026-06-24T17:05:30","modified_gmt":"2026-06-24T14:05:30","slug":"masters-degree-awarded-to-ms-israa-wazkool-in-cybersecurity","status":"publish","type":"post","link":"https:\/\/su.edu.ye\/voga\/en\/23073\/","title":{"rendered":"Master\u2019s Degree Awarded to Ms. Israa Wazkool in Cybersecurity"},"content":{"rendered":"

Ms. Israa Abd Wazkool \u200e<\/strong>was awarded a Master\u2019s Degree in Cybersecurity <\/strong>for her thesis titled: A Machine Learning-Based Model for Attack Detection in Cloud Computing Environments<\/em><\/strong>, <\/em>which was submitted to the Department of Computer Science, Faculty of Computer and Information Technology \u2013 Sana\u2019a University. The MA defense was held on Thursday, June 18, 2026\u200e\u200e.<\/strong><\/p>\n

\u00a0<\/strong><\/p>\n

The MA Viva-voce Committee, which was formed based on a resolution issued by the Graduate Studies and Scientific Research Council, consisted of the following:<\/p>\n\n\n\n\n\n\n
#<\/strong><\/td>\nCommittee Members<\/strong><\/td>\nDesignation<\/strong><\/td>\nPosition<\/strong><\/td>\n<\/tr>\n
1<\/td>\nProf. Abdulmajeed Ahmed Ghaleb Al-Khulaidi<\/strong><\/td>\nInternal Examiner<\/td>\nChair<\/td>\n<\/tr>\n
2<\/td>\nProf. Abdulrahman Ahmed Ali Al-Sabri<\/strong><\/td>\nMain Supervisor<\/td>\nMember<\/td>\n<\/tr>\n
3<\/td>\nAssoc. Prof. Jameel Rashid Salman Qaid<\/strong><\/td>\nExternal Examiner<\/td>\nMember<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

The thesis aimed to:<\/p>\n

    \n
  • Develop a machine learning-based model to enhance the performance of intrusion detection systems in cloud computing environments through building an ensemble model capable of improving detection accuracy and reducing false alarm rates and strengthening the ability of cloud-based security systems to detect various types of cyberattacks.<\/li>\n<\/ul>\n

    The study yielded several key findings summarized as follows:<\/p>\n

      \n
    • The proposed model achieved a high level of accuracy and efficiency in detecting cyberattacks.<\/li>\n
    • The ensemble model outperformed several individual classifiers across different performance metrics.<\/li>\n
    • The model significantly improved the detection of minority attack classes and effectively addressed the problem of imbalanced datasets.\u200e<\/li>\n<\/ul>\n

      In light of these findings, the researcher recommended the following:\u200e<\/p>\n

        \n
      • Adopting ensemble-learning models in cloud-based intrusion detection systems.<\/li>\n
      • Regularly updating training datasets to keep pace with the evolving nature of cyber threats.<\/li>\n
      • Evaluating the proposed model in real-world cloud environments using live data streams.<\/li>\n
      • Expanding the use of advanced artificial intelligence techniques to further strengthen cloud-computing security.<\/li>\n<\/ul>\n

        The defense session was attended by a number of academics, researchers, students, colleagues, and the researcher\u2019s family.<\/p>\n

         <\/p>\n

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