[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122162-en":3,"doc-seo-122162-105":30,"detail-sidebar-cat-0-en-105":83},{"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":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},122162,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A Robust Machine Learning Framework for Fraudulent Mobile App Detection - VFAST Transactions on Software Engineering Volume 12 - Issue 4 - October-December 2024","Rapid growth of mobile applications has increased the prevalence of fraudulent apps, creating serious exposure to financial loss and potential security compromise. A “Fraud App Detection” framework is proposed to build a reliable system that recognizes and categorizes fraudulent applications using advanced machine learning and artificial intelligence. The workflow gathers data, preprocesses apps, extracts features, trains multiple models, and evaluates performance via recall, accuracy, and F1-score. Neural networks, decision trees, and ensemble methods enhance detection efficiency and accuracy, strengthening mobile app security protocols and safeguarding users from likely threats.","VFAST Transactions on Software Engineering Volume 12, Issue 4, 2024  \nVFAST Transactions on Software Engineering  \n[https://vfast.org/journals/index.php/VTSE@ 2024](https://vfast.org/journals/index.php/VTSE@ 2024), ISSN(e): 2309-3978, ISSN(p): 2411-6246  \nVolume 12, Number 4, October-December 2024 pp: 27-36  \nKeywords: Fraud App Detection, Artificial Intelligence, Machine Learning, Mobile Applications, Cybersecurity, Fraud Detection, Neural Networks, Decision Trees, Ensemble Methods, Feature Extraction  \nJournal Info:  \nSubmitted: October 08, 2024 Accepted:  \nNovember 08, 2024 Published:  \nNovember 12, 2024  \nA Robust Machine Learning Framework for Fraudulent Mobile App Detection  \nHassan Zaki  \n1*  \n,  \nMuhammad Saad  \n1  \n,  \nMuhammad Rehan Rasheed  \n2  \n1 Faculty of Software Engineering, Sir Syed University of Engineering & Technology, Pakistan;  \n2 Faculty of Computer Engineering, Sir Syed University of Engineering & Technology, Pakistan  \nAbstract  \nThe rapid development of mobile applications has led to a significant rise in the number of fraudulent applications. The biggest risk now is financial loss and possible security compromise. Thus, the \"Fraud App Detection\" framework goal is to develop a reliable system that can recognize and categorize fraudulent apps utilizing cutting-edge machine learning and artificial intelligence approaches. The process of identifying fraudulent patterns involves gathering data, preprocessing applications, extracting features, and training several machine learning models. The model’s performance will be assessed based on evaluation criteria like recall, accuracy, and F1-score. To improve detection efficiencyand accuracy, this uses cutting-edge techniques such as neural networks, decision trees, and ensemble approaches. These results can be used in enhancing mobile app security protocols, thus safeguarding consumers from the probable threats of fraudulent applications.  \n*Correspondence author email address: [hzaki@ssuet.edu.pk](hzaki@ssuet.edu.pk)[ ](hzaki@ssuet.edu.pk)DOI: 10.21015/vtse.v12i4 .1931  \n1 Introduction  \nThe way we engage with technology has changed dramatically in recent years due to the widespread use of mobile applications (apps), which provide efficiencyand convenience in a variety of fields. Alongside genuine apps, there has been a noticeable increase in fraudulent ones, which pose serious risks like lost money, compromised user privacy, and data breaches. Therefore, it is now critical to identify and remove these fraudulent programmes in order to guarantee the security and reliability of mobile platforms.  \nThe goal of this research, \"Fraud App Detection,\" is  \nto use cutting-edge machine learning (ML) and artificial intelligence (AI) [1–5] techniques to address these issues. The main goal is to create a reliable and precise system that can automatically distinguish and categories phone apps from authentic ones. Through the use of AI and ML, we hope to improve the efficacy andefficiency of fraud detection mechanisms in the realm of mobile applications.  \nThis exponential growth in mobile applications went hand in hand with increased fraudulent presence within app stores. Concretely, some methods researched by the investigators include sophisticated  \nThis work is licensed under a Creative Commons Attribution 3.0 License.   \nVFAST Transactions on Software Engineering Volume 12, Issue 4, 2024  \nAI and ML algorithms and conventional rule-based systems for fake app identification. Early research focused on creating a rule-based system 1 where predefined parameters were implemented that signal unusual behaviors within apps. The new ones utilize AI and ML algorithms, which run through large datasets in search of irregularities that may raise red flags for fraud through reviews by users, app information, and transaction patterns.  \nAI and ML algorithms[6, 7]have shown potential in fraud detection from different areas, such as mobile apps. Some of the used supervised learning algorithms in cate","cbCaiibAeTGy9d7M","https://ap.wps.com/l/cbCaiibAeTGy9d7M","pdf",213859,1,10,"English","en",105,"# Abstract\n# Introduction\n## Problem context: growth of mobile apps and fraud risk\n## Research goal: AI/ML-based fraudulent app identification\n## Related approaches: rule-based vs AI/ML methods\n## Challenges: evolving fraud and class imbalance\n## Dataset and evaluation preparation","[{\"question\":\"Which datasets are used to construct the training and evaluation set?\",\"answer\":\"Data samples are taken from Kaggle, the UC Machine Learning Repository, and the Canadian Institute for Cybersecurity Dataset IDS 2017, as listed in the document text.\"}]","A Robust Machine Learning Framework for Fraudulent Mobile App Detection - VFAST Transactions on Software Engineering Volume 12 - Issue 4 - October-December 2024 | PDF",1785809133,25,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"a-robust-machine-learning-framework-for-fraudulent-mobile-app-detection-vfast-transactions-on-software-engineering-volume-12-issue-4-october-december-2024","",{"@graph":36,"@context":77},[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/a-robust-machine-learning-framework-for-fraudulent-mobile-app-detection-vfast-transactions-on-software-engineering-volume-12-issue-4-october-december-2024/122162/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Which datasets are used to construct the training and evaluation set?","Question",{"text":75,"@type":76},"Data samples are taken from Kaggle, the UC Machine Learning Repository, and the Canadian Institute for Cybersecurity Dataset IDS 2017, as listed in the document text.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]