[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120544-en":3,"doc-seo-120544-105":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},120544,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Artificial Intelligence-Driven Detection of Android Malware Using Machine Learning Techniques - Research Focus","The rapid growth of Android smartphones and their open-source ecosystem make them a frequent target for malware, threatening user privacy and device security. As malware evolves, traditional detection techniques struggle to maintain accuracy against new patterns and also suffer from high false positives, performance limitations, and weak scalability. This study applies machine learning with scalable practices for effective Android malware detection using a dataset of 15,000+ benign and malicious apps.","Vol. 15 (2025) No. 5 ISSN: 2088-5334  \nArtificial Intelligence-Driven Detection of Android Malware Using  \nMachine Learning Techniques  \nAmir Muhammad Hafiz Othmana,b,1, Mohd Faizal Ab Razaka, Salwana Mohamad @ Asmara a,  \nSofia Najwa Ramli c,2  \na Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia b Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia c Centre for CyberSecurity and Data Intelligence, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia [Corresponding author:](Corresponding author:1 ohafiz00@gmail.com)[1](Corresponding author:1 ohafiz00@gmail.com)[ ohafiz00@gmail.com](Corresponding author:1 ohafiz00@gmail.com); [2](2 sofianajwa@uthm.edu.my)[ sofianajwa@uthm.edu.my](2 sofianajwa@uthm.edu.my)  \nAbstract—The rapid expansion of Android smartphones and their open-source characteristics have rendered them a primary target for malware attacks, endangering user privacy and device security. With the rise of malware attacks, robust and reliable security is not only wanted but much needed. As the Android malware landscape evolves, traditional approaches become increasingly challenging to implement with the same degree of accuracy in detecting new, emerging malware patterns. Recently, machine learning has gained attention as an effective and reliable solution in many application domains, including malware detection. However, traditional methods for detecting malware on Android smartphones are facing new challenges, such as a high false positive rate, performance problems, and a lack of scalability. In this study, we investigate the application of machine learning-based systematic practices to achieve effective and scalable Android malware detection. The experiments were conducted using a dataset consisting of over 15,000 benign and malicious Android apps. A SelectKBest feature selection method was used to reduce computation and improve efficiency by extracting the top 20 most relevant features. The extracted features were then used to train and validate four machine learning classifiers: KNearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP). The model was cross-validated using K-fold cross-validation to evaluate its performance on the entire dataset. Random Forest has achieved the highest accuracy among other models, which is 90.64%. The result demonstrates the feasibility of implementing static analysis feature selection and machine learning methods to improve the detection accuracy and computation efficiency. The study provides practical guidance for an optimized static analysis approach that focuses on manifest permissions, enhances detection accuracy, and reduces computational overhead for Android-based malware detection systems, thereby protecting mobile cybersecurity.  \nKeywords—Android malware detection; machine learning; static analysis; cybersecurity.  \nManuscript received 8 Apr. 2025; revised 17 Jul. 2025; accepted 26 Sep. 2025. Date of publication 31 Oct. 2025.  \nIJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nAndroid devices have rapidly become the dominant mobile operating system market, with 6.06 billion malware attacks detected worldwide [1], [2], [3] This popularity stems from their affordability, flexibility, and open ecosystem, making them an essential part of daily life for individuals and businesses. Over the past decade, Android has successfully secured 87.8% of the global smartphone market share, highlighting its dominance in the mobile industry [4], [5] . However, these same qualities also make Android a prime target for cybercriminals. Malware, such as spyware, adware, and trojans, exploits Android's vulnerabilities to steal data, disrupt services, and gain unauthorized access to sensitive information [6], [7], [8] .  \nThe second quarter of 2022 prevented around 5.5 m","cbCairudVd48gJi0","https://ap.wps.com/l/cbCairudVd48gJi0","pdf",1150921,1,7,"English","en",105,"# Introduction\n## Android threat landscape and malware risks\n## Limitations of traditional detection approaches\n# Methodology and Experiment Design\n## Dataset composition\n## SelectKBest feature selection\n## Classifiers and cross-validation\n# Results and Discussion\n## Model performance and accuracy\n## Static analysis benefits and efficiency","[{\"question\":\"Why is Android malware detection becoming more challenging over time?\",\"answer\":\"Android malware patterns evolve, reducing the effectiveness of traditional detection methods and increasing false positives. The approaches also face performance and scalability constraints when new threats appear.\"},{\"question\":\"What dataset and feature selection approach were used in the study?\",\"answer\":\"The experiments used a dataset with over 15,000 benign and malicious Android apps. SelectKBest feature selection was applied to extract the top 20 most relevant features to improve efficiency.\"},{\"question\":\"Which machine learning models were evaluated and what was the best-performing one?\",\"answer\":\"The study trained and validated KNN, SVM, Random Forest, and MLP, using K-fold cross-validation. Random Forest achieved the highest accuracy at 90.64%.\"}]","Artificial Intelligence-Driven Detection of Android Malware Using Machine Learning Techniques - Research Focus | PDF",1785730582,18,{"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},"artificial-intelligence-driven-detection-of-android-malware-using-machine-learning-techniques-research-focus","",{"@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/artificial-intelligence-driven-detection-of-android-malware-using-machine-learning-techniques-research-focus/120544/",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","2026-08-03",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},"Why is Android malware detection becoming more challenging over time?","Question",{"text":75,"@type":76},"Android malware patterns evolve, reducing the effectiveness of traditional detection methods and increasing false positives. The approaches also face performance and scalability constraints when new threats appear.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and feature selection approach were used in the study?",{"text":80,"@type":76},"The experiments used a dataset with over 15,000 benign and malicious Android apps. SelectKBest feature selection was applied to extract the top 20 most relevant features to improve efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models were evaluated and what was the best-performing one?",{"text":84,"@type":76},"The study trained and validated KNN, SVM, Random Forest, and MLP, using K-fold cross-validation. 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