[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122750-en":3,"doc-seo-122750-105":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},122750,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Defending Your Mobile Fortress - An In-Depth Look at on-Device Trojan Detection in Machine Learning - Systematic Literature Review","Mobile app trojans increasingly threaten personal information security by exposing sensitive, personally identifying data to malicious actors. The review consolidates trojan attack vectors and mitigation approaches, focusing on risk elimination and how automated, evidence-based detection can be generated. A PRISMA-guided state-of-the-art review analyzes literature and examines the role of machine learning in on-device trojan detection. Findings support the effectiveness of machine learning and highlight signature-based analysis using permission, intent, API and system calls, and network behavior before and after initial infection.","JPPIPA 9(7) (2023)  \nJurnal Penelitian Pendidikan IPA  \nJournal of Research in Science Education  \n[http://jppipa.unram.ac.id/index.php/jppipa/index](http://jppipa.unram.ac.id/index.php/jppipa/index)  \nDefending Your Mobile Fortress: An In-Depth Look at onDevice Trojan Detection in Machine Learning: Systematic Literature Review  \nLila Setiyani1, Koo Tito Novelianto2, Rusdianto Roestam2, Sella Monica2, Ayu Nur Indahsari2, Amadeuz Ezrafel2, Alinda Endang Poerwati2, Yuliarman Saragih3*  \n1Information Systems Study Program, STMIK ROSMA, Karawang, Indonesia  \n2Information Study Program Informatics, Universitas President, Bekasi, Indonesia  \n3Electrical Engineering Study Program, Universitas Singaperbangsa, Karawang, Indonesia  \nReceived: May 10, 2023  \nRevised: July 12, 2023  \nAccepted: July 25, 2023  \nPublished: July 31, 2025  \nCorresponding Author: Yuliarman Saragih  \n[yuliarman@gmail.com](yuliarman@gmail.com)  \nDOI: 10.29303/jppipa.v9i7.4209  \n© 2023 The Authors. This openaccess article is distributed under a (CC-BY License)  \nAbstract: Mobile app trojans are becoming an increasingly serious threat to personal information security. They can cause severe damage by exposing sensitive and personally-identifying information to malicious actors. This paper’s contribution is a comprehensive review of the attack vectors for trojan attacks, and ways to eliminate the risks posed by attack vectors and generate settlement automatically. As such, such attacks must be prevented. In this study, we explore to find how to detect the trojan attack in detail, and the way that we know in machine learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and metaanalyses (PRISMA) guidelines. We review literature from several publications and analyze the use of machine learning for on-device trojan detection. This review provides evidence for the effectiveness of machine learning in detecting such threats. The current trend shows that signature-based analysis using various metadata, such as permission, intent, API and system calls, and network analysis, are capable of detecting trojan attacks before and after the initial infection.  \nKeywords: Machine Learning; on-device detection; PRISMA: Trojan  \nIntroduction  \nThe development of mobile devices has brought security challenges, reports from Weichbroth & Łysik (2020), explained that mobile devices such as Android have become one of the main targets for attackers to spread mobile malware, especially Trojan viruses. In the context of security evaluation Riadi et al. (2022) describes a trojan attack capable of stealing mobile device user credentials such as important information including system information, contacts, call logs, messages, and full access to the victim device's system directory. In a literature review conducted by Alzubaidi (2021), malware such as trojan viruses infect Android mobile devices via Google Play. The rise of cybercrimes targeting Android devices with Malware,(Saeed Jawad & Hlayel, 2022) informed one of the most popular  \n___________  \nmalware of which is the Remote Access Trojan (RAT) which allows potential malicious users to control the system remotely, malware infection according to (Du et al., 2022) can be caused by social engineering, besides that malware developers use Fully Undetected (FUD) techniques this makes users unable to detect it.  \nAnother study conducted by Zhao (2022), informs that hackers have made a lot of efforts to produce malware and find mobile device vulnerabilities, therefore an understanding of the concept of trojan malware infection, and mobile device vulnerabilities need to be understood by users. The issue of preventing cell phone viruses is very important. The development of handling mobile device vulnerabilities from trojan malware has been carried out by applying detection using machine learning. Mcdonald et al. (2021a), proposes the naïve Bayes algorithm to mine trojan  \nHow to Cite:  \nSeti","cbCailXd4cBnTeKO","https://ap.wps.com/l/cbCailXd4cBnTeKO","pdf",530266,1,7,"English","en",105,"# Introduction\n## Mobile malware threats and trojan infection\n## Detection challenges and prevention importance\n## Machine learning-based approaches\n# Systematic Literature Review Method\n## PRISMA guideline application\n## Literature selection and analysis\n# Machine Learning for On-Device Detection\n## Signature-based and metadata-driven analysis\n## Effectiveness before and after initial infection","[{\"question\":\"What threat do mobile app trojans pose to users?\",\"answer\":\"Mobile app trojans can expose sensitive, personally identifying information to malicious actors, leading to severe damage to personal information security.\"},{\"question\":\"How does the paper structure its review process?\",\"answer\":\"The study conducts a systematic literature review following PRISMA guidelines, surveying state-of-the-art methods and analyzing how machine learning is used for on-device trojan detection.\"},{\"question\":\"Which indicators are emphasized for detecting trojans on-device?\",\"answer\":\"Detection can rely on signature-based analysis using metadata such as permissions, intents, APIs and system calls, together with network analysis before and after initial infection.\"}]","Defending Your Mobile Fortress - 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