[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120473-en":3,"doc-seo-120473-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120473,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Development of a Machine Learning-Based Cyber Threat Intelligence Dashboard System for Strategic Operations","Cyber Threat Intelligence (CTI) has become a core capability for cybersecurity experts as attacks grow in frequency and sophistication. Integrating machine learning into CTI strengthens detection beyond conventional rule-based approaches that struggle with emerging threats and rapidly changing attacker strategies. This paper evaluates machine-learning-driven CTI dashboard systems and proposes a user-friendly platform for real-time threat detection, analysis, and visualization using Gradient Boosting Trees (GBT). The implemented system achieves 99.6% precision, 99.5% recall, 99.97% F1-score, and an average response time of 3 minutes 12 seconds.","LAUTECH Journal of Engineering and Technology 19 (3) 2025: 169-179  \n10.36108/laujet/5202.91.0361  \nDevelopment of a Machine Learning-Based Cyber Threat Intelligence Dashboard System for Strategic Operations  \nCentre  \n1Gadzama E. H., 1Saidu I. R., 2Alhassan J. K. and 3Odion P. O.  \n1Department of Cyber Security, Nigerian Defence Academy, Kaduna, Nigeria 2Department of Cyber Security Science, Federal University of Technology, Minna, Nigeria 3Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria  \n\n| Article Info |  ABSTRACT  Cyber Threat Intelligence (CTI) has become an essential element in the toolkit of Cybersecurity experts. In recent years, the significance of CTI has grown exponentially due to the increasing sophistication and frequency of cyber attacks.\u003Cbr>The incorporation of machine learning methodologies into CTI systems represents a substantial advancement in the domain. Conventional rule-based systems frequently fall short in identifying emerging threats and adjusting to the swiftly evolving strategies employed by cybercriminals. This paper presents a systematic appraisal of CTI dashboard systems that incorporate machine learning techniques to enhance strategic cybersecurity operations, which provide a user-friendly platform for real-time threat detection, analysis, and visualisation. At the core of this study is the utilisation of Gradient Boosting Trees (GBT) as the primary machine learning algorithm for threat detection and classification. The research only focused on the detection, analysis, and presentation of threat intelligence, leaving the specific response strategies at the discretion of the organisation implementing the system. The CTI dashboard system, which is the result of this work, showed strong performance, with a precision of 99.6%, a recall of 99.5%, and an F1-score of 99.97%. The system also showed an average response time of 3 minutes and 12 seconds, demonstrating its effectiveness in delivering timely and accurate threat intelligence. |\n| --- | --- |\n| Article history:\u003Cbr>Received: May 7, 2025\u003Cbr>Revised: June 22, 2025\u003Cbr>Accepted: June 28, 2025 |  |\n| Keywords:\u003Cbr>Cybersecurity, Dashboard, Threat Intelligence, Machine Learning, Operations Centre\u003Cbr>Corresponding Author:\u003Cbr>[gadzamahe@nda.edu.ng](gadzamahe@nda.edu.ng);+2348126980414 |  |\n\nINTRODUCTION  \nCybersecurity has undergone a profound evolution in recent years, driven by the growing complexity and prevalence of cyber threats. The digital era has brought about unparalleled connectivity and technological progress; however, it has simultaneously rendered both individuals and organisations vulnerable to a wide array of cyber risks. The advent of big data has further amplified the potential of CTI systems. Nassar and Kamal (2021) noted that the sheer volume, velocity, and variety of data generated in modern networks provide both challenges and opportunities. While processing this data manually is unfeasible, machine learning models can sift through vast datasets to  \nextract meaningful insights and detect subtle indicators of compromise. The true value ofCTI lies not just in data collection and analysis, but in its presentation and actionability. Karlsson et al. (2021) emphasised the importance of intuitive dashboards that can distil complex threat data into comprehensible visualisations and actionable intelligence. Such dashboards enable stakeholders across an organisation, from security analysts to Csuite executives, to grasp the current threat landscape and make informed decisions rapidly. The development of CTI systems that leverage machine learning and provide user-friendly interfaces represents a convergence of multiple technological domains. Montasari et al. (2021) observe that the development ofCTI using machine  \nlearning requires expertise in data science, cybersecurity, software engineering, and user experience design. This multidisciplinary approach is essential to create systems that are not only technically ro","cbCaipi9dApCYrkv","https://ap.wps.com/l/cbCaipi9dApCYrkv","pdf",865730,1,11,"English","en",105,"# Introduction\n## Context and growing need for CTI\n## Machine learning for data-driven threat detection\n## Dashboard usability and strategic decision support\n## Related work and representative platforms","[{\"question\":\"What problem does the CTI dashboard system address?\",\"answer\":\"It targets the need to detect, analyze, and present cyber threat intelligence in a timely and actionable way for strategic cybersecurity operations.\"},{\"question\":\"Which machine learning algorithm is used for threat detection and classification?\",\"answer\":\"The study uses Gradient Boosting Trees (GBT) as the primary machine learning algorithm for detection and classification.\"},{\"question\":\"How well does the proposed system perform?\",\"answer\":\"It reports 99.6% precision, 99.5% recall, 99.97% F1-score, and an average response time of 3 minutes 12 seconds.\"}]","Development of a Machine Learning-Based Cyber Threat Intelligence Dashboard System for Strategic Operations | 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