[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117350-en":3,"doc-seo-117350-105":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},117350,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhancing Financial Cybersecurity via Advanced Machine Learning - Analysis, Comparison","Financial institutions face escalating cyber-attacks due to the sensitive nature of handled data and the growing scale and sophistication of threats. This paper compares six widely used machine learning techniques for preventing cyber-attacks in the financial industry, evaluates model efficacy and scalability, and focuses on practical deployment considerations. It also proposes a framework to integrate the best-performing model into existing cybersecurity infrastructure to improve resilience. Results show XGBoost achieving 95% accuracy.","Enhancing financial cybersecurity via advanced machine learning: analysis, comparison  \nGrace Odette Boussi1, Himanshu Gupta2, Syed Akhter Hossain3  \n1Department of Information Technology, Amity University, Noida, India 2Department of Information Technology, Faculty of Cyber Security, Amity University, Noida, India 3Department of Computer Science and Engineering, University of Liberal Arts, Dhaka, Bangladesh  \n\n| Article history:\u003Cbr>Received Apr 22, 2024 Revised Nov 14, 2024 Accepted Nov 24, 2024 | The financial sector is a prime target for cyber-attacks due to the sensitive nature of the data it handles. As the frequency and sophistication of cyber threats continue to rise, implementing effective security measures becomes paramount. In this paper we provide a comprehensive comparison of six prominent machine learning techniques utilized in the financial industry for cyber-attack prevention. The study aims to identify the best-performing model and subsequently compares its performance with a proposed model tailored to the specific challenges faced by financial institutions. This paper looks at using advanced machine learning methods to make cybersecurity stronger for financial institutions. The work explores the deployment of cutting-edge machine learning algorithms - logistic regression, random forest, support vector machines (SVM), K-nearest neighbour (KNN), naïve Bayes, extreme gradient boosting (XGBoost), and deep learning technique (Dense Layer) - to fortify the cybersecurity framework within financial institutions. Through a meticulous analysis and comparative study, we explore the efficacy, scalability, and practical implementation aspects of various machine learning algorithms tailored to address cybersecurity concerns. Additionally, we propose a framework for integrating the most effective machine learning models into existing cybersecurity infrastructure, offering insights into bolstering resilience against evolving cyber threats. In our comparison, XGBoost exhibited outstanding performance with an accuracy of 95% .\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Cybersecurity\u003Cbr>Deep learning\u003Cbr>Extreme gradient boosting Machine learning Malware |  |\n\nCorresponding Author:  \nGrace Odette Boussi  \nDepartment of Information Technology, Amity University Noida sector 143, 201301, Uttar Pradesh, India  \nEmail: [graceboussi@gmail.com](graceboussi@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe digital landscape has made significant advancements, especially online, where a majority of our activities take place, due to the creative methods employed by attackers, the risk of cyberattacks is rapidly increasing [1] . Rapid technological evolution and increasing internet users, reaching 4.4 billion in 2019, are expected to rise post-COVID-19. With online services holding sensitive data, attackers increasingly target hacking such platforms [2] . In today's digital era, the financial sector operates within an intricate web of interconnected systems and processes, making it a prime target for cyber threats of unprecedented sophistication and scale [3] . As digital transactions, sensitive financial data, and complex networks become increasingly common, traditional cybersecurity measures often prove insufficient in protecting against evolving threats. Consequently, financial institutions are under growing pressure to strengthen their defenses  \nand mitigate the risks posed by cyber-attacks. In response to this urgent need, there is a rising interest in harnessing advanced machine learning techniques to enhance cybersecurity within the financial sector. These technologies play a crucial role in the implementation of cyber defense strategies such as monitoring, control, threat detection, and alarm systems [4] . The adoption of machine learning in cybersecurity has witnessed significant growth in popularity [5] . The current state of financial cybersecurity underscores the ess","cbCaibiyapahVVyq","https://ap.wps.com/l/cbCaibiyapahVVyq","pdf",590764,1,9,"English","en",105,"# Keywords\n## Cybersecurity\n## Deep learning\n## Extreme gradient boosting\n## Machine learning\n## Malware\n# 1. Introduction\n## Cyber risks in finance\n## Machine learning for defense strategies\n## Malware and detection priorities","[{\"question\":\"Why is financial cybersecurity a prime target for cyber-attacks?\",\"answer\":\"The financial sector handles sensitive data and relies on interconnected systems and transactions. Attackers increasingly target these platforms with threats that grow in sophistication and scale.\"},{\"question\":\"Which machine learning techniques are compared for cyber-attack prevention?\",\"answer\":\"The study compares logistic regression, random forest, support vector machines, K-nearest neighbor, naïve Bayes, extreme gradient boosting (XGBoost), and a deep learning Dense Layer approach.\"},{\"question\":\"What is the main outcome of the comparison study?\",\"answer\":\"XGBoost delivers outstanding performance, achieving 95% accuracy, and the paper uses these findings to propose an integration framework into existing cybersecurity infrastructure.\"}]","Enhancing Financial Cybersecurity via Advanced Machine Learning - Analysis, Comparison | PDF",1785675312,23,{"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},"enhancing-financial-cybersecurity-via-advanced-machine-learning-analysis-comparison","",{"@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/enhancing-financial-cybersecurity-via-advanced-machine-learning-analysis-comparison/117350/",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","2026-08-02",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 is financial cybersecurity a prime target for cyber-attacks?","Question",{"text":75,"@type":76},"The financial sector handles sensitive data and relies on interconnected systems and transactions. Attackers increasingly target these platforms with threats that grow in sophistication and scale.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques are compared for cyber-attack prevention?",{"text":80,"@type":76},"The study compares logistic regression, random forest, support vector machines, K-nearest neighbor, naïve Bayes, extreme gradient boosting (XGBoost), and a deep learning Dense Layer approach.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main outcome of the comparison study?",{"text":84,"@type":76},"XGBoost delivers outstanding performance, achieving 95% accuracy, and the paper uses these findings to propose an integration framework into existing cybersecurity infrastructure.","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"]