[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118324-en":3,"doc-seo-118324-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118324,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning approaches for malware classification in Android platform - a review","The rapid growth of Android applications has driven a steady increase in Android malware. Machine learning has been identified as an effective and promising approach for Android malware detection. This review surveys Android malware detection methodologies grounded in machine learning, first outlining Android application background, system architecture, and security mechanisms, and defining malware categories. It then synthesizes the research landscape across sample acquisition, data preprocessing, feature selection, model and algorithm design, and evaluation of detection effectiveness to guide future studies.","Machine learning approaches for malware classification in android platform: a  \nreview  \nABSTRACT  \nThe rapid growth of Android applications has led to a continuous influx of Android malware. Numerous research has been undertaken to tackle that issue. Existing research has indicated that leveraging machine learning is a highly effective and promising approach for Android malware detection. This paper presents a review of Android malware detection methodologies that rely on machine learning. We commence by providing a brief overview of the background context related to Android applications, including insights into the Android system architecture, security mechanisms, and the categorization of Android malware. Subsequently, with machine learning as the central focus, we methodically examine and condense the current state of research, encompassing crucial perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the assessment of detection effectiveness. The aim of this review is to equip scholars with a holistic understanding of Android malware detection through the lens of machine learning. It is intended to serve as a foundational resource for future researchers embarking on new endeavours in this field, while also providing overarching guidance for research endeavours within the broader domain.","cbCaiki0yxYQhsbZ","https://ap.wps.com/l/cbCaiki0yxYQhsbZ","pdf",37328,1,"English","en",105,"# Abstract\n# Background: Android applications and security\n## Android system architecture\n## Android security mechanisms\n## Categorization of Android malware\n# Machine learning-driven review of detection methods\n## Sample acquisition\n## Data preprocessing\n## Feature selection\n## Machine learning models and algorithms\n## Evaluation of detection effectiveness\n# Purpose and contribution","[{\"question\":\"What problem does this review focus on?\",\"answer\":\"It focuses on Android malware classification and detection in response to the continuous increase of Android malware caused by the rapid growth of Android applications.\"},{\"question\":\"What aspects of the machine learning pipeline does the review summarize?\",\"answer\":\"It summarizes research across sample acquisition, data preprocessing, feature selection, choice of machine learning models and algorithms, and assessment of detection effectiveness.\"},{\"question\":\"What is the goal of the review for readers?\",\"answer\":\"It aims to provide scholars with a holistic understanding of machine-learning-based Android malware detection to support future research directions and guidance in the broader field.\"}]","Machine learning approaches for malware classification in Android platform - 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