MARC details
| 000 -LEADER |
|---|
| fixed length control field |
06672namaa22004331i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
EG-GICUC |
| 005 - أخر تعامل مع التسجيلة |
|---|
| control field |
20260209155945.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260209s2025 ua a|||frm||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloguing agency |
EG-GICUC |
| Language of cataloging |
eng |
| Transcribing agency |
EG-GICUC |
| Modifying agency |
EG-GICUC |
| Description conventions |
rda |
| 041 0# - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| Language code of summary or abstract |
eng |
| -- |
ara |
| 049 ## - Acquisition Source |
|---|
| Acquisition Source |
Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.404076 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Classification number |
658.404076 |
| Edition number |
21 |
| 097 ## - Degree |
|---|
| Degree |
Ph.D |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| Local Call Number |
Cai01.18.06.Ph.D.2025.Ma.E |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Authority record control number or standard number |
Mahmoud Kamal Eldin Elsayed Said Bakhaty, |
| Preparation |
preparation. |
| 245 14 - TITLE STATEMENT |
|---|
| Title |
The effect of ai and big data analytics on improving cyber vulnerabilities management in critical infrastructure / |
| Statement of responsibility, etc. |
by Mahmoud Kamal Eldin Elsayed Said Bakhaty ; Supervision Prof. Dr. Essam Ali Amin, Dr. Mohamed Abdulla Ewees. |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
تأثير الذكاء الإصطناعى وتحليل البيانات الضخمة على تحسين إدارة الثغرات السيبرانية للبنية التحتية الحرجة |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2025. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
81 Leaves : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
CD. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rda content |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
Unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Ph.D)-Cairo University, 2025. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography: pages 74 -77. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
In the contemporary digital landscape, effective cyber vulnerability management (VM) is critical <br/>for safeguarding Critical Infrastructure (CI) against evolving cyber threats. This study introduces <br/>a sophisticated Decision Support System (DSS) that integrates Big Data Analytics (BDA) and <br/>Artificial Intelligence (AI), including Natural Language Processing (NLP) and Named Entity <br/>Recognition (NER), to revolutionize VM practices. By leveraging tailored VM methodologies <br/>and a custom dataset representing organizational assets, the proposed DSS delivers actionable <br/>insights through interactive dashboards, ensuring accurate vulnerability identification and timely <br/>mitigation. The system's AI model demonstrates exceptional performance, with a precision score <br/>of 95.39%, recall of 96.55%, and an F-score of 95.97%, reflecting its capability to identify <br/>vulnerabilities accurately while minimizing false positives and overlooked threats. The DSS <br/>dynamically adapts to organizational environments, enhancing interoperability across <br/>heterogeneous data formats and incorporating insights from diverse sources. These capabilities <br/>enable organizations to optimize security operations, improve risk management, and strengthen <br/>cyber resilience. The research methodology included a comprehensive survey involving 72 <br/>cybersecurity experts. Participants engaged with the system through hands-on demonstrations <br/>and detailed exploration, followed by a Likert-scale evaluation. The survey findings validated the <br/>system’s effectiveness, confirming four key hypotheses: (H1) VM implementation positively <br/>impacts CI cybersecurity, (H2) AI and BDA improve VM time efficiency, (H3) AI and BDA <br/>reduce VM costs, and (H4) AI and BDA enhance VM quality. The results emphasize the critical <br/>role of AI and BDA in delivering faster, more accurate, and cost-effective vulnerability <br/>identification and mitigation. Beyond improving VM processes, the DSS addresses broader <br/>organizational needs, including optimizing human resources, supporting informed procurement <br/>decisions, and improving risk management in multi-project environments. These align with <br/>principles of Strategic Alignment, Resource Optimization, and Performance Measurement, <br/>further demonstrating the system’s practical value. This study contributes to the emerging field of <br/>DSS in cybersecurity by presenting a robust, AI-driven framework tailored for VM in CI. The <br/>findings highlight the system’s potential to not only enhance VM practices but also to drive <br/>strategic decision-making, operational efficiency, and cyber resilience. The integration of <br/>cutting-edge technologies underscores the relevance of this DSS as a comprehensive solution to <br/>address the complexities of modern cybersecurity challenges. |
| 520 #3 - SUMMARY, ETC. |
|---|
| Summary, etc. |
في ظل التهديدات السيبرانية المتزايدة، تمثل إدارة الثغرات السيبرانية عنصرًا حيويًا لحماية البنية التحتية الحرجة. تقدم هذه الدراسة نظام دعم قرار (DSS) متقدم يعتمد على الذكاء الاصطناعي (AI) وتحليلات البيانات الضخمة (BDA)، بما يشمل تقنيات معالجة اللغة الطبيعية (NLP) والتعرف على الكيانات (NER)، بهدف تطوير ممارسات إدارة الثغرات. يعتمد النظام على مجموعة بيانات مخصصة لأصول المؤسسة ومنهجيات متقدمة لتحديد الثغرات بدقة، ويعرض نتائج التحليل من خلال لوحات معلومات تفاعلية. حقق النظام دقة بلغت 95.39% واسترجاعًا بنسبة 96.55%، مما يدل على كفاءته في تقليل الإيجابيات الكاذبة وتحسين الاستجابة. شارك 72 خبيرًا في الأمن السيبراني في تقييم النظام من خلال عروض عملية واستطلاع باستخدام مقياس ليكرت. وأكدت النتائج أربع فرضيات رئيسية، أهمها أن تقنيات AI وBDA تُحسن الكفاءة وتقلل التكاليف وتعزز جودة إدارة الثغرات. يساهم النظام كذلك في تحسين الموارد البشرية، ودعم قرارات المشتريات، وتعزيز إدارة المخاطر، بما يتماشى مع مبادئ المواءمة الاستراتيجية وقياس الأداء. تقدم هذه الدراسة نموذجًا مبتكرًا يعزز اتخاذ القرار ويعالج تحديات الأمن السيبراني الحديثة بكفاءة ومرونة. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Issues CD |
Issues also as CD. |
| 546 ## - LANGUAGE NOTE |
|---|
| Text Language |
Text in English and abstract in Arabic & English. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Project Management |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
ادارة المشروعات |
| 653 #1 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
cyber vulnerability management |
| -- |
Cyber Security |
| -- |
Big Data Analytics |
| -- |
Artificial Intelligence |
| -- |
إدارة الثغرات السيبرانية |
| -- |
الأمن السيبراني |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Essam Ali Amin |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mohamed Abdulla Ewees |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
|---|
| Grant date |
01-01-2025 |
| Supervisory body |
Essam Ali Amin |
| -- |
Mohamed Abdulla Ewees |
| Universities |
Cairo University |
| Faculties |
Faculty of Graduate Studies for Statistics Research |
| Department |
Department of Project Management |
| 905 ## - Cataloger and Reviser Names |
|---|
| Cataloger Name |
Shimaa |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| Edition |
21 |
| Suppress in OPAC |
No |