[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117536-en":3,"doc-seo-117536-105":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},117536,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","ANDROID MALWARE PREDICTION USING MACHINE LEARNING","Increasing daily malware exploiting the Internet poses a serious threat to users and organizations. Manual malicious software identification is no longer sufficient because malware is highly prevalent and constantly evolving. This thesis investigates automatic, behavior-based malware detection using machine learning. Multiple algorithms—including Decision Tree, Random Forest, Logistic Regression, SVM, and KNN—are evaluated for classifying five families: Adware, Benign, Ransomware, SMS Malware, and Scareware. Experimental results show practical effectiveness, reaching 71% accuracy.","REPUBLIC OF TÜRKİYE ALTINBAŞ UNIVERSITY  \nInstitute of Graduate Studies  \nElectrical and Computer Engineering  \nANDROID MALWARE PREDICTION USING  \nMACHINE LEARNING  \nSari Khdhaer MUKHLIF  \nMaster’s Thesis  \nSupervisor  \nAsst.Prof. Dr. Sefer KURNAZ  \nIstanbul, 2023  \nANDROID MALWARE PREDICTION USING MACHINE LEARNING  \nSari Khdhaer MUKHLIF  \nElectrical and Computer Engineering  \nMaster’s Thesis  \nALTINBAŞ UNIVERSITY  \nThe thesis titled ANDROID MALWARE PREDICTION USING MACHINE LEARNING prepared by SARİ KHDHAER MUKHLIF and submitted on 13 /4 /2023 has been accepted unanimously for the degree of Master of Science in Electrical and Computer Engineering.  \nAsst. Prof. Dr. Sefer KURNAZ  \nSupervisor  \nThesis Defense Committee Members  \nAsst. Prof. Dr. Sefer KURNAZ Department of Computer  \nAsst. Prof. Dr. Oğuz KARAN  \nEngineering,  \nAltınbaş University  \nDepartment of Software  \nEngineering,  \nAltınbaş University  \n__________________  \n__________________  \nAsst. Prof. Dr. Serdar KARGIN Department of  \nBiomedical Engineering,  \nArel University    \nI hereby declare that this thesis meets all format and submission requirements of a Master’s thesis.  \nSubmission date of the thesis to Institute of Graduate Studies:  / /   \nI hereby declare that all information presented in this graduation project has been obtained in full accordance with academic rules and ethical conduct. I also declare all unoriginal materials and conclusions have been cited in the text and all references mentioned in the Reference List have been cited in the text, and vice versa as required by the abovementioned rules and conduct.  \nSari MUKHLIF  \nSignature  \nDEDICATION  \nFirst, I would like to thank Allah Almighty for the power of the mind, health, strength, guidance, knowledge, and skills to complete this study.  \nThis thesis is wholeheartedly dedicated to my parents. There are no words to describe what you mean to me; there is nothing that I can repay for what you have done to me. I will continue to do my best to achieve your expectations.  \nLastly, I dedicated this to the family, relatives, and friends who have been encouraging me during this study.  \nACKNOWLEDGEMENTS  \nI would like to thank my supervisor Asst. Prof. Dr. Sefer KURNAZ for all support during my study. Its great pleasure to express my deepest gratitude to my friends who have shared with me best moments during my study for the Master degree.  \nABSTRACT  \nANDROID MALWARE PREDICTION USING MACHINE LEARNING  \nMUKHLIF, Sari Khdhaer  \nM.Sc., Electrical and Computer Engineering, Altınbaş University,  \nSupervisor: Asst. Prof. Dr. Sefer KURNAZ  \nDate: April / 2023  \nPages: 38  \nIncreasing daily malware that exploits the Internet has become a severe threat. The manual malicious software (a.k.a., malware) identification is no longer valid and effective due to the high prevalence of malware. Thus, automatic behavior-based malware detection using machine learning techniques seems an effective solution. Various studies have proven the efficiency of machine learning to detect and classify malware files. In this research, several machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, SVM, and KNN, have been investigated to detect malicious software applications. For classification purpose, five classes, namely Adware, Benign, Ransomware, SMS Malware, and Scareware, have been used. Experimental results demonstrated that machine learning algorithms are practical and efficient for malware detection, and 71% accuracy can be achieved with the help of these algorithms.  \nKeywords: Machine Learning, Ensemble Learning, Android Malware, Android Malware Attack, Static Analysis, Dynamic Analysis, Multiple Classifier Systems.  \nTABLE OF CONTEINTS  \nPages  \nABSTRACT .................................................................................................................................. vii  \nLIST OF TABLES .....................................................................................","cbCaiiiWWahZL1zl","https://ap.wps.com/l/cbCaiiiWWahZL1zl","pdf",455898,1,44,"English","en",105,"# ABSTRACT\n# 1. INTRODUCTION\n# 2. 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Automatic behavior-based detection is presented as a stronger solution.\"},{\"question\":\"Which machine learning algorithms were investigated for Android malware detection?\",\"answer\":\"The study evaluates Decision Tree, Random Forest, Logistic Regression, SVM, and KNN for detecting malicious applications.\"},{\"question\":\"What malware categories were used for classification?\",\"answer\":\"Five classes are used: Adware, Benign, Ransomware, SMS Malware, and Scareware.\"}]","ANDROID MALWARE PREDICTION USING MACHINE LEARNING | PDF",1785676769,111,{"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},"android-malware-prediction-using-machine-learning","",{"@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/android-malware-prediction-using-machine-learning/117536/",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 manual malware identification considered insufficient in this research?","Question",{"text":75,"@type":76},"Manual identification is no longer valid and effective due to the high prevalence and evolution of malware. 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