[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124824-en":3,"doc-seo-124824-105":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},124824,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering - A Review","Review of state-of-the-art hybrid machine learning approaches for email and malware spam filtering. The work formulates and addresses three research questions by systematically collecting and analyzing related studies from major scientific databases using defined inclusion and exclusion criteria. The synthesis highlights common hybrid ML algorithms used to improve email spam filtering, and summarizes the current datasets employed for both email and malware spam filtering. The review consolidates evidence and clarifies research directions for hybrid filtering pipelines.","Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review  \nUgwueze Walter Oluchukwu, Anigbogu Sylvanus Okwudili, Asogwa Doris Chinedu  , Emmanuel Chibuogu Asogwa 􀀍  , Anigbogu Kenechukwu Sylvanus Department of Computer Science, NnamdiAzikiwe University Awka, Nigeria  \n\n| Suggested Citation |\n| --- |\n| Oluchukwu, U.W., Okwudili, A.S., Chinedu, A.D., Asogwa, E.С . & Sylvanus, A.K. (2024) . Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review. European Journal of Theoretical and Applied Sciences, 2(2), 76-86.\u003Cbr>DOI: 10.59324/ejtas.2024.2(2).07 |\n\nAbstract:  \nIn this paper, we presented a review of the state-of-the-art hybrid machine learning algorithms that were being used for email effective computing. For this reason, three research questions were formed, and the questions were answered by studying and analyzing related papers collected from some well-established scientific databases (Springer Link, IEEE Explore, Web of Science, and Scopus) based on some exclusion and inclusion criteria. The result presented the common Hybrid ML algorithms used to enhance email spam filtering. Also, the state-of-the-art datasets used for email and malware spam filtering were presented.  \nKeywords: Email Effective Computing, Email and Malware Spam Filtering, Machine Learning Algorithms.  \nIntroduction  \nMachine learning algorithms have been widely used for email and malware spam filtering. Several studies have evaluated the performance of different machine learning models in this task. The Naïve Bayes classifier, support vector machine (SVM), neural network, and decision tree are commonly used algorithms for spam email evaluation (Abdulhamid, Adetunmbi, & Ajibuwa, 2019) . These algorithms have demonstrated good performance in identifying spam emails based on their content and sender information.  \nDynamic malware detection techniques using machine learning algorithms have also been explored (Gupta, Gupta, & Gupta, 2020) . Classifiers such as kNN, DT, RF, AdaBoost, SGD, extra trees, and Gaussian NB have achieved high accuracy in detecting malware based on their behaviors. These techniques  \nanalyze the behavior of suspected malware in a sandbox environment, allowing them to detect even zero-day attacks that have not yet been identified by traditional signature-based antivirus software.  \nAdditionally, machine-learning techniques have been applied to detect spam messages (Almomani, et al., 2022) . Support vector machines, k-Nearest Neighbor, Naïve Bayes, neural networks, recurrent neural networks, Ada Boost, random forest, gradient boosting, logistic regression, and decision trees have been tested for spam detection, with satisfactory results in terms of accuracy. These techniques can effectively identify spam messages based on their content, sender information, and other characteristics.  \nHybrid spam emails, which combine both image and text parts, pose a challenge for traditional filters (Hnini, et al., 2022) . Late multi-modal fusion training frameworks using convolutional  \nneural networks (CNN) and a continuous bag of words have been proposed to address this issue. These frameworks can effectively extract and analyze both visual and textual features from hybrid spam emails, improving overall spam filtering performance.  \nSuperior Performance in Malware Detection  \nMachine learning methods, such as decision trees and random forests, have shown superior performance in detecting malware, achieving 100% accuracy in some studies (Gupta, Gupta,& Gupta, 2022) . These methods can effectively identify malicious software based on its code, behavior, and other characteristics. In ALHawamleh, (2023) the provided study does not specifically discuss hybrid machine-learning algorithms for email and malware spam filtering.  \nIn their study, the authors’ modified optimization-based feature selection method was used for spam classification, which achieved a high level of accuracy in spam classification us","cbCaic6CjDzhj7pQ","https://ap.wps.com/l/cbCaic6CjDzhj7pQ","pdf",328825,1,11,"English","en",105,"# Introduction\n## Machine Learning for Spam Email and Malware Detection\n## Challenges of Hybrid (Image+Text) Spam and Multimodal Approaches\n# Superior Performance in Malware Detection\n# Methodology\n## Systematic Literature Review Approach\n## Eligibility Criteria\n## Search String and Data Sources","[{\"question\":\"What is the purpose of the review on hybrid machine learning for spam filtering?\",\"answer\":\"The review summarizes state-of-the-art hybrid machine learning algorithms used for email spam filtering and malware spam filtering. It consolidates evidence from collected research studies to answer formulated research questions.\"},{\"question\":\"How were studies selected for the review?\",\"answer\":\"A systematic literature review methodology was used. Inclusion required English peer/blind-reviewed work between 2019 and 2023 and relevance to hybrid machine-learning algorithms for email and malware spam filtering; exclusion removed non-English and unrelated papers or those lacking explicit contributions in the abstract.\"},{\"question\":\"Which databases and search terms were used to find relevant papers?\",\"answer\":\"The review searched well-established scientific databases including SpringerLink, IEEE Xplore, Web of Science, and Scopus. Search strings were constructed using phrases such as “Email spam,” “Malware Filtering,” “Hybrid machine learning for email spam,” and “review on malware filtering”.\"}]","Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering - A Review | PDF",1785894842,28,{"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},"hybrid-machine-learning-algorithms-for-email-and-malware-spam-filtering-a-review","",{"@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/hybrid-machine-learning-algorithms-for-email-and-malware-spam-filtering-a-review/124824/",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","2026-08-05",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},"What is the purpose of the review on hybrid machine learning for spam filtering?","Question",{"text":75,"@type":76},"The review summarizes state-of-the-art hybrid machine learning algorithms used for email spam filtering and malware spam filtering. It consolidates evidence from collected research studies to answer formulated research questions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were studies selected for the review?",{"text":80,"@type":76},"A systematic literature review methodology was used. Inclusion required English peer/blind-reviewed work between 2019 and 2023 and relevance to hybrid machine-learning algorithms for email and malware spam filtering; exclusion removed non-English and unrelated papers or those lacking explicit contributions in the abstract.",{"name":82,"@type":73,"acceptedAnswer":83},"Which databases and search terms were used to find relevant papers?",{"text":84,"@type":76},"The review searched well-established scientific databases including SpringerLink, IEEE Xplore, Web of Science, and Scopus. 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