[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119525-en":3,"doc-seo-119525-105":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},119525,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Using Machine Learning to Detect Unauthorized Access in Database's Log Files - Volume 5 - Issue 6","The paper investigates the use of machine learning techniques to detect unauthorized access in database log files. Experimental results indicate that most supervised learning algorithms identify normal cases effectively but face difficulty when detecting anomalies. Naïve Bayes and Random Forest show mediocre performance, identifying only one out of twenty anomalies. For semi-supervised methods, Local Outlier Factor reaches high accuracy for normal cases and moderate accuracy for anomalies, while One Class Support Vector Machine and Isolation Forest perform poorly on anomalies. Overall, semi-supervised techniques may be more effective for unauthorized access detection.","JOURNAL LA MULTIAPP  \nVOL. 05, ISSUE 06 (824-832), 2024  \nDOI: 10.37899/journallamultiapp.v5i6 .1538  \nUsing Machine Learning to Detect Unauthorized Access in Database's Log Files  \nIsraa Jihad Abed1  \n1 University of Al-Anbar, Iraq  \n*Corresponding Author: Israa Jihad Abed  \nEmail: [israajehaad@gmail.com](israajehaad@gmail.com)  \n| Article Info  Article history: | Abstract \u003Cbr>The paper investigates the use of machine learning techniques to detect |\n| --- | --- |\n| Received 17 August 2024 | unauthorized access in database log files. Results show that most |\n| Received in revised form 06 | algorithms of supervised machine learning performed well in identifying |\n| September 2024 | normal cases but struggled to detect anomalies, with the exception of |\n| Accepted 19 September 2024 | Naïve Bayes and Random Forest which gave mediocre results by |\n| Keywords:\u003Cbr>Anomaly Detection Log Files\u003Cbr>Machine Learning Unauthorized Access | identifying one out of twenty anomalies. In the semi-supervised machine learning methods, Local Outlier Factor showed an accuracy of 0.98 in detecting normal cases and 0.7 in detecting anomalies. One Class Support Vector Machine had an accuracy of 0.89 for normal cases and 0.05 for anomalies, while Isolation Forest had an accuracy of 0.98 for normal cases and 0.0 for anomalies. These findings suggest that semisupervised techniques may be more effective in detecting unauthorized access in database log files. |\n\nIntroduction  \nIn today’s digital age, databases contain a wealth of sensitive information, making them prime sources of unauthorized access by malicious people. Unauthorized access to databases can have serious consequences, from data breaches to lost revenue and reputational damage. Consequently, it is important for organizations to implement strong security measures to detect and prevent unauthorized access. One way to increase database security is to use machine learning techniques to analyze log files for suspicious activity. Machine learning algorithms can help identify patterns and anomalies in log data that may indicate unauthorized access. By real-time detection and unauthorized action, organizations can reduce the risks associated with data breaches and protect their valuable information.  \nThis paper explores the importance of recognizing access to databases and the role of machine learning to improve security measures. We will also discuss existing methods for detecting unauthorized access to databases, highlighting their strengths and limitations. Unauthorized access to databases can have significant consequences for organizations, including financial loss, legal liabilities, and reputational damage. Determining accessibility is essential to protect sensitive information and ensure compliance with data protection laws. By effectively monitoring database activity and detecting suspicious behavior, organizations can prevent data breaches and reduce the risks associated with cyber threats. Several methods have been developed to detect unauthorized access to databases, such as rule-based policies, anomaly detection, and audit log analysis s and sources.  \nLiterature Review  \nUnauthorized database access is a serious security risk that can result in sensitive data theft, data breaches, and other malicious activity. Devices with learning have become a common 824  \nISSN: 2716-3865 (Print), 2721-1290 (Online)  \nCopyright © 2024, Journal La Multiapp, Under the license CC BY-SA 4.0  \ntool for spotting illegal access to database systems in recent years. To find access to database systems, machine learning methods including clustering, classification, and anomaly detection have been applied. Anomaly detection techniques, such One-Class SVM and Isolation Forest, are frequently used to find unusual values in database log files. These systems can detect anomalous user activity and notify administrators of possible security threats.  \nTo categorize people as permitted or prohibited, classification met","cbCaiukdp7phxz93","https://ap.wps.com/l/cbCaiukdp7phxz93","pdf",599764,1,9,"English","en",105,"# Introduction\n## Literature Review\n## Results and Discussion","[{\"question\":\"Why are database log files important for detecting unauthorized access?\",\"answer\":\"Databases store sensitive information, and log files capture user actions and system events. Monitoring and analyzing these logs helps reveal suspicious behavior linked to unauthorized access.\"},{\"question\":\"How did supervised machine learning models perform in anomaly detection?\",\"answer\":\"Supervised algorithms generally performed well on identifying normal cases, but they struggled to detect anomalies, limiting their effectiveness for unauthorized access detection.\"},{\"question\":\"Which semi-supervised method showed the strongest ability to detect unauthorized behavior?\",\"answer\":\"Local Outlier Factor achieved high accuracy for normal cases and moderate accuracy for anomalies, suggesting semi-supervised techniques can be more effective than purely supervised approaches for this task.\"}]","Using Machine Learning to Detect Unauthorized Access in Database's Log Files - Volume 5 - Issue 6 | PDF",1785724774,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-machine-learning-to-detect-unauthorized-access-in-databases-log-files-volume-5-issue-6","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-to-detect-unauthorized-access-in-databases-log-files-volume-5-issue-6/119525/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are database log files important for detecting unauthorized access?","Question",{"text":75,"@type":76},"Databases store sensitive information, and log files capture user actions and system events. Monitoring and analyzing these logs helps reveal suspicious behavior linked to unauthorized access.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did supervised machine learning models perform in anomaly detection?",{"text":80,"@type":76},"Supervised algorithms generally performed well on identifying normal cases, but they struggled to detect anomalies, limiting their effectiveness for unauthorized access detection.",{"name":82,"@type":73,"acceptedAnswer":83},"Which semi-supervised method showed the strongest ability to detect unauthorized behavior?",{"text":84,"@type":76},"Local Outlier Factor achieved high accuracy for normal cases and moderate accuracy for anomalies, suggesting semi-supervised techniques can be more effective than purely supervised approaches for this task.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]