{"id":20811,"date":"2025-12-18T12:43:24","date_gmt":"2025-12-18T09:43:24","guid":{"rendered":"https:\/\/su.edu.ye\/voga\/?p=20811"},"modified":"2025-12-18T12:48:13","modified_gmt":"2025-12-18T09:48:13","slug":"masters-degree-awarded-to-ms-haifa-hamoud-thabet-in-information-technology","status":"publish","type":"post","link":"https:\/\/su.edu.ye\/voga\/en\/20811\/","title":{"rendered":"Master\u2019s Degree Awarded to Ms. Haifa Hamoud Thabet in Information Technology"},"content":{"rendered":"

Ms. Haifa Hamoud Thabit Al-Hammadi <\/strong>was awarded a Master\u2019s Degree in Information Technology <\/strong>for her thesis titled: Enhancing IoT Security Anomaly Detection Based on FusionNet<\/em><\/strong>, which was submitted to the Department of Information Technology, Faculty of Computer and Information Technology \u2013 Sana\u2019a University. The MA defense was held on Tuesday, December 2, 2025.<\/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. Sharaf Abdulhaq Al-Hamdi<\/strong><\/td>\nInternal Examiner<\/td>\nChair<\/td>\n<\/tr>\n
2<\/td>\nAssoc. Prof. Ibrahim Ahmed Al-Balata<\/strong><\/td>\nMain Supervisor<\/td>\nMember<\/td>\n<\/tr>\n
3<\/td>\nAssoc. Prof. Farouk Abdu Al-Fuhaidi<\/strong><\/td>\nExternal Examiner<\/td>\nMember<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The study aimed to enhance the security of Internet of Things (IoT) devices by improving the FusionNet model for anomaly and attack detection, achieving high accuracy and dynamic adaptability to evolving threat patterns.<\/p>\n

The study yielded a number of key findings summarized as follows:<\/p>\n

    \n
  • The proposed model improved overall security performance in IoT environments by enhancing anomaly and attack detection capabilities.<\/li>\n
  • The model achieved improved performance metrics in identifying anomalies and cyberattacks compared to baseline approaches.<\/li>\n
  • The system demonstrated effective adaptive response mechanisms to varying risk levels in medical IoT environments, enabling dynamic threat mitigation.<\/li>\n<\/ul>\n

    In light of these findings, the researcher recommended further studies on:<\/p>\n

      \n
    • Integrating the proposed model with more advanced security technologies to further enhance detection accuracy and system robustness.<\/li>\n
    • Conducting comparative studies with a wider range of advanced models using diverse datasets from industrial, smart city, and other IoT environments.<\/li>\n<\/ul>\n

      The defense session was attended by faculty members, researchers, students, colleagues, and the researcher\u2019s family.<\/p>\n

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