[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123405-en":3,"doc-seo-123405-105":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":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},123405,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Static Analysis-based Detection of Android Malware using Machine Learning Algorithms","Rapid growth of Android applications has increased security threats, making malware detection a critical concern in cybersecurity. The research proposes a static-analysis method that applies machine learning to identify Android malware. Static permission features are extracted from APK files and evaluated with three classifiers: SVM, Random Forest, and Decision Tree. Using a dataset of 400 applications (200 benign, 200 malicious), the models are compared with accuracy, precision, recall, and F1-score, with Random Forest achieving the highest accuracy. Results show static analysis plus robust classification can detect malicious apps effectively, though obfuscated and dynamic threats remain challenging, motivating future hybrid work.","Static Analysis-based Detection of Android Malware using Machine Learning Algorithms  \n1Omar Emad Saied*, 2Karam H. Thanoon  \n1Department of Software, College of Computer Science and Mathematics, University of Mosul, Iraq 2Department of Cyber Security, College of Computer Science and Mathematics, University of Mosul,  \nIraq  \n*[e-mail:](e-mail:1Omaremad_gold@uomosul.edu.iq)[1](e-mail:1Omaremad_gold@uomosul.edu.iq)[Omaremad_gold@uomosul.edu.iq](e-mail:1Omaremad_gold@uomosul.edu.iq), [2](2karamhatim@uomosul.edu.iq)[karamhatim@uomosul.edu.iq](2karamhatim@uomosul.edu.iq)  \n(received: 5 July 2025, revised: 19 July 2025, accepted: 20 July 2025)  \nAbstract  \nThe rapid growth of Android applications has led to increased security threats, making malware detection a critical concern in cybersecurity. This research proposes a static analysis-based technique that employs machine learning for Android malware detection. The proposed method utilizes three classification algorithms: Support Vector Machine (SVM), Random Forest, and Decision Tree. The tool extracts static permission features from APK files to evaluate their effectiveness. The dataset consists of 400 Android applications (200 benign and 200 malicious), which were analyzed using the three machine learning models. Their performance was evaluated and compared using accuracy , precision, recall, and F1-score. The Random Forest model achieved the highest accuracy. The results demonstrate that static analysis combined with a robust classification model can effectively identify malicious applications with a high degree of accuracy. Although the tool is reliable in detecting Android malware, it has limitations in handling obfuscated and dynamic threats. Future research could focus on integrating dynamic analysis techniques to improve detection accuracy and enhance resistance to evasion techniques.  \nKeywords: android malware, static analysis, machine learning, support vector machine, random forest, decision tree, cybersecurity  \n1 Introduction  \nSoftware security involves protecting software from various types of malware and is a crucial aspect of cybersecurity. Initially, hacking was mainly considered a prank targeting victims’machines. However, with the continuous evolution of internet services the motivation behind cyberattacks shifted from mere amusement to financial gain and other valuable assets [1] .  \nMobile devices have become essential for various services such as communication, entertainment, financial transactions, and education. Software applications are now deeply integrated into daily life, influencing critical domains like traffic control, aviation, and self-driving cars [2] . For many people, life without these devices is unimaginable.  \nAndroid is one of the most popular and widely used mobile operating systems, consistently evolving and gaining popularity. Modern applications and devices such as smartphones, laptops, printers, and scanners have introduced several security challenges [3] . According to StatCounter, Android dominated the global smartphone operating system market in 2023, accounting for approximately 71.74%[4] .  \nBecause of its open-source nature and frequent updates by a vast developer community, Android has become a primary target for cyberattacks, mainly through malicious applications (malware) . Android malware has evolved rapidly, adopting sophisticated techniques such as encryption and obfuscation to conceal its malicious intent.  \nAttackers can be individuals or organized groups, including former intelligence operatives or independent hackers. In general, hackers are categorized into two main groups: black-hat hackers, who exploit vulnerabilities for malicious purposes, and white-hat hackers, who work to improve system security [5] .  \nStatic analysis is one of the primary methods used to detect malware on Android devices. It involves extracting permission features from an APK application to identify malicious behavior, as shown in Figure 1. Howe","cbCaihc9peRjt7K5","https://ap.wps.com/l/cbCaihc9peRjt7K5","pdf",1163697,1,12,"English","en",105,"# Abstract\n# 1 Introduction\n## Android security threats and malware evolution\n## Static analysis for Android malware detection\n## Limitations: obfuscation and polymorphism\n## Static vs dynamic analysis approaches","[{\"question\":\"What is the main goal of the proposed method?\",\"answer\":\"To detect Android malware using a static analysis approach combined with machine learning classifiers.\"},{\"question\":\"Which machine learning algorithms are used in the study?\",\"answer\":\"Support Vector Machine (SVM), Random Forest, and Decision Tree are used for classification.\"},{\"question\":\"How are permissions used for malware detection?\",\"answer\":\"The tool extracts static permission features from APK files and uses them as input to the machine learning models to classify apps.\"}]","Static Analysis-based Detection of Android Malware using Machine Learning Algorithms | PDF",1785816313,30,{"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},"static-analysis-based-detection-of-android-malware-using-machine-learning-algorithms","",{"@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/static-analysis-based-detection-of-android-malware-using-machine-learning-algorithms/123405/",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",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed method?","Question",{"text":75,"@type":76},"To detect Android malware using a static analysis approach combined with machine learning classifiers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used in the study?",{"text":80,"@type":76},"Support Vector Machine (SVM), Random Forest, and Decision Tree are used for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How are permissions used for malware detection?",{"text":84,"@type":76},"The tool extracts static permission features from APK files and uses them as input to the machine learning models to classify apps.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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"]