[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290862-105":59,"doc-detail-290862-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","efficient-cyber-threat-intelligence-automation-using-machine-learning-algorithm-msc-research-project","Efficient Cyber Threat Intelligence automation using Machine Learning algorithm - MSc Research Project","","This research focuses on enhancing Cyber Threat Intelligence (CTI) automation capabilities through Machine Learning. CTI collects, structures, detects, and analyses logs gathered from Network Traffic Analysis tools such as Snort. Logs generated via self-simulated threat incidents on a local Kali Linux virtual machine are parsed into required formats and analysed with unsupervised algorithms including k-means clustering. Results are visualised on a dashboard to support businesses and small IT companies deploying automated CTI systems.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/efficient-cyber-threat-intelligence-automation-using-machine-learning-algorithm-msc-research-project/290862/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/efficient-cyber-threat-intelligence-automation-using-machine-learning-algorithm-msc-research-project/290862.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the research address?","Question",{"text":112,"@type":113},"It targets improving the automation capabilities of Cyber Threat Intelligence by applying Machine Learning to process and analyse threat-related logs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the CTI logs generated and prepared for analysis?",{"text":117,"@type":113},"Snort generates unstructured logs from self-simulated threat incidents on a local Kali Linux virtual machine, and the logs are parsed into a required structure for analysis.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning approach is used and how are results presented?",{"text":121,"@type":113},"Unsupervised learning, including k-means clustering, analyses the structured logs, and the resulting data is presented graphically using a dashboard.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290862,1789642838,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Efficient Cyber Threat Intelligence automation using Machine Learning  \nalgorithm  \nMSc Research Project MSc. in Cybersecurity  \nLikhith Umesh Salian  \nStudent ID: 23205237  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Khadija Hafeez  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Likhith Umesh Salian  \nStudent ID: 23205237 Programme: [MSc. in](MSc. in) Cybersecurity Year: 2024  \nModule: Practicum Part 2  \nSupervisor: Khadija Hafeez  \nSubmission Due  \nDate: 12/12/2024  \nProject Title: Efficient Cyber Threat Intelligence automation using machine  \nlearning algorithm  \nWord Count: 7274 Page Count: 19  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: LIKHITH UMESH SALIAN  \nDate: 12/12/2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nEfficient Cyber Threat Intelligence automation using Machine Learning algorithm  \nLikhith Umesh Salian  \n23205237  \nAbstract  \nThis research attains the focus towards achieving the goal of enhancing the Cyber Threat Intelligence (CTI) automation capabilities by utilising Machine Learning. The CTI aims towards collecting, structuring, detecting, and analysing, the logs gathered from the Network Traffic Analysis tools like Snort. The Information Technology industry constantly faces high severity threats, considering the importance of ensuring the preparedness towards the various cyber threats occurring online. A Network Intrusion detection system will largely help in the detection and analysis of the suspected paranormal events by analysing the behavioural patterns in the logs. The gathered unstructured logs are generated through Snort by self-simulated threat incident from a local Kali Linux virtual machine. The logs are parsed changed to required structure of format which shall be analysed using the Unsupervised machine learning algorithm like k-means clustering algorithm. The resulting data is then represented graphically using a dashboard. This proposed model based on the K-means algorithms aims to provide security solution to businesses and small-scale IT companies in need to deploy its own automated CTI systems.  \n1 Introduction  \n1.1 Research Background  \nIn the cyber space, we observe that every day there are various events happen that may have occurred due to a possible incident. The incidents may have either occurred due to a possible cyber-attack or from a mistake by an insider. These incidents are recorded in the computer systems on consecutively as “logs”. The cyber investigator will monitor the log data when an incident is reported by the security analyst. The log data consists of the incident details such as date, time, location, source IP address, and destination IP address, where the incident has been reported.  \n1.2 Importance  \nThe Cyber Thre","cbCaiizJN6Z15k1o","https://ap.wps.com/l/cbCaiizJN6Z15k1o","pdf",1188402,21,"English","# Abstract\n# 1 Introduction\n## 1.1 Research Background\n## 1.2 Importance","[{\"question\":\"What problem does the research address?\",\"answer\":\"It targets improving the automation capabilities of Cyber Threat Intelligence by applying Machine Learning to process and analyse threat-related logs.\"},{\"question\":\"How are the CTI logs generated and prepared for analysis?\",\"answer\":\"Snort generates unstructured logs from self-simulated threat incidents on a local Kali Linux virtual machine, and the logs are parsed into a required structure for analysis.\"},{\"question\":\"Which machine learning approach is used and how are results presented?\",\"answer\":\"Unsupervised learning, including k-means clustering, analyses the structured logs, and the resulting data is presented graphically using a dashboard.\"}]","Efficient Cyber Threat Intelligence automation using Machine Learning algorithm - MSc Research Project | PDF",53]