[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117809-en":3,"doc-seo-117809-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117809,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Applying Machine Learning Tools to Detect Cyber Attacks in Financial Firms and Banks - Study Notes","Machine learning is presented as an increasingly important approach for detecting cyber attacks in financial firms and banks, offering greater scalability, efficiency, and actionability than traditional, human-dependent methods. Multiple techniques, including deep learning, support vector machines, and Bayesian classification, are reviewed, and XGBoost is recommended for high performance. The work frames the goal as identifying and preventing online threats to banking information systems, supporting stronger protection measures and faster, real-time responses while reducing manual intervention.","Applying Machine Learning Tools to Detect Cyber Attacks in Financial Firms and Banks  \nAuthor: MA Islam Supervisor: Dr Kashinath Basu  \nSchool of Engineering, Computing and  \nMathematics  \n Introduction   \nThe use of machine learning in cybersecurity is becoming increasingly important for detecting cyber attacks in financial firms and banks. Machine learning offers improved scalability, efficiency, and actionability compared to traditional methods that rely on human interaction. Various machine learning techniques, including deep learning, support vector machines, and Bayesian classification, have shown promise in detecting cyber attacks. This study uses machine-learning techniques and tools to detect cyber attacks in financial firms and banks, and recommends the use of XGBoost due to its high performance. Ensuring cybersecurity in financial firms and banks is crucial for maintaining the integrity, confidentiality, and transparency of transactions in virtual and online banking systems.  \n Research Question   \nWhat is the best machine learning model financial companies (banks) have used to prevent information systems from online threats?  \n Research Aim   \nAnalyzing cyberthreat challenges to banks is the primary objective of this research, which further proposes a method for identifying and preventing the cyberattacks.  \nScope of Research   \n• To help company leaders implement effective protection measures against cybercrime for profitable business procedures and beneficial social reform.  \n• To help smaller financial companies and other enterprises guard against damaging cyber security incidents.  \n• Highlight the importance of ICT use for society, business as well as government in Bangladesh's Internet group.  \n Methodology   \n 1. Decision Making  \n2. Data Collection  \n 3. Data Cleaning  \n4. Data Modelling  \n 5. Data Visualization   \n 6. Conclusion   \nOutcomes  \nConclusion and Recommendation  \nThe study explored machine learning methods for detecting and combating cyber threats in the financial services industry. They suggest that implementing machine learning algorithms such as deep learning and XGBoost for fraud detection in banks can help to swiftly identify suspicious activity, confirm user identities, and respond to cyberattacks. Additionally, ML reduces the need for human intervention by scanning vast volumes of data in real-time and improves user experience by streamlining identity verification procedures.  \nReferences: Ali, L. (2019) . Cybercrime is a growing menace to the commercial sectors that is constantly there (a study of the online banking sectors in GCC) . Journal of Developing Areas, 53, 267-279 . doi:10 . 1353/jda. 2019 .0016  \nAl-Hamar, A. K. (2016) . enhancing Qatari organization's information security procedures. doi:10 .5339/qfarc. 2016 . ICTPP2531 . Qatar Foundation Annual Research Conference Proceedings, 2016 (1), p. ICTPP2531 .  \nM. C. Cant and J. A. Wiid (2013) . identifying the difficulties facing SMEs in South Africa. Journal of International Business and Economics, Volume 12, Pages 707–716 . Obtainable at [http://www.cluteinstitute.com](http://www.cluteinstitute.com)  \nNumber of different attacks done/attempted by several malwares are stated below:  \nMalware detection rate by different approaches are as follows:  \nDifferent types of attacks and normal access :","cbCaikYLDaQkYTUm","https://ap.wps.com/l/cbCaikYLDaQkYTUm","pdf",474429,1,"English","en",105,"# Introduction\n# Research Question and Aim\n# Scope of Research\n# Methodology\n## Decision Making\n## Data Collection\n## Data Cleaning\n## Data Modelling\n## Data Visualization\n# Outcomes\n# Conclusion and Recommendation\n# References","[{\"question\":\"Why is machine learning important for cybersecurity in financial firms and banks?\",\"answer\":\"Machine learning improves scalability, efficiency, and actionability compared with traditional methods, enabling faster detection and response in virtual and online banking systems.\"},{\"question\":\"What model is recommended in the study for cyber-attack detection?\",\"answer\":\"The study recommends XGBoost due to its high performance, alongside other approaches such as deep learning for fraud and threat detection.\"},{\"question\":\"What is the main objective of the research?\",\"answer\":\"The primary objective is to analyze cyberthreat challenges to banks and propose a method for identifying and preventing cyberattacks.\"}]","Applying Machine Learning Tools to Detect Cyber Attacks in Financial Firms and Banks - 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