ReckDroid:检测安卓应用程序中的红包欺诈行为

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computers & Security Pub Date : 2024-09-12 DOI:10.1016/j.cose.2024.104117
{"title":"ReckDroid:检测安卓应用程序中的红包欺诈行为","authors":"","doi":"10.1016/j.cose.2024.104117","DOIUrl":null,"url":null,"abstract":"<div><p>Recently, red packets have appeared widely in various mobile apps. Related security issues like fraud are gradually coming into the public eye. As a new means of fraud, red packet fraud has not yet been explored or addressed. In this paper, based on our empirical study on red packets, we propose a novel approach ReckDroid for red packet fraud detection. Our approach adopts a heuristic algorithm to identify red packets and then detects red packet fraud by analyzing the network traffic dynamically generated during the automated exploration of mobile apps. Our experiments are performed on hundreds of labeled real-world apps. Experimental results show that ReckDroid identifies red packets with a precision of 98.0% and a recall of 93.3%, and detects red packet fraud with a precision of 88.6% and a recall of 92.5%. By applying ReckDroid to over 1000 Android apps in the wild, we find that apps with red packets account for 17.6% of apps from seven app markets (including Google Play) while red packet fraud mainly occurs in Chinese app markets.</p></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ReckDroid: Detecting red packet fraud in Android apps\",\"authors\":\"\",\"doi\":\"10.1016/j.cose.2024.104117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recently, red packets have appeared widely in various mobile apps. Related security issues like fraud are gradually coming into the public eye. As a new means of fraud, red packet fraud has not yet been explored or addressed. In this paper, based on our empirical study on red packets, we propose a novel approach ReckDroid for red packet fraud detection. Our approach adopts a heuristic algorithm to identify red packets and then detects red packet fraud by analyzing the network traffic dynamically generated during the automated exploration of mobile apps. Our experiments are performed on hundreds of labeled real-world apps. Experimental results show that ReckDroid identifies red packets with a precision of 98.0% and a recall of 93.3%, and detects red packet fraud with a precision of 88.6% and a recall of 92.5%. By applying ReckDroid to over 1000 Android apps in the wild, we find that apps with red packets account for 17.6% of apps from seven app markets (including Google Play) while red packet fraud mainly occurs in Chinese app markets.</p></div>\",\"PeriodicalId\":51004,\"journal\":{\"name\":\"Computers & Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Security\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S016740482400422X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Security","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016740482400422X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

最近,红包广泛出现在各种移动应用程序中。欺诈等相关安全问题也逐渐进入公众视野。作为一种新的欺诈手段,红包欺诈尚未被探索和解决。本文基于对红包的实证研究,提出了一种新颖的红包欺诈检测方法 ReckDroid。我们的方法采用启发式算法来识别红包,然后通过分析自动探索移动应用程序过程中动态生成的网络流量来检测红包欺诈。我们在数百个贴有标签的真实应用程序上进行了实验。实验结果表明,ReckDroid 识别红包的精确度为 98.0%,召回率为 93.3%;检测红包欺诈的精确度为 88.6%,召回率为 92.5%。通过将 ReckDroid 应用于 1000 多款野生安卓应用,我们发现在七个应用市场(包括 Google Play)中,有红包的应用占 17.6%,而红包欺诈主要发生在中国应用市场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ReckDroid: Detecting red packet fraud in Android apps

Recently, red packets have appeared widely in various mobile apps. Related security issues like fraud are gradually coming into the public eye. As a new means of fraud, red packet fraud has not yet been explored or addressed. In this paper, based on our empirical study on red packets, we propose a novel approach ReckDroid for red packet fraud detection. Our approach adopts a heuristic algorithm to identify red packets and then detects red packet fraud by analyzing the network traffic dynamically generated during the automated exploration of mobile apps. Our experiments are performed on hundreds of labeled real-world apps. Experimental results show that ReckDroid identifies red packets with a precision of 98.0% and a recall of 93.3%, and detects red packet fraud with a precision of 88.6% and a recall of 92.5%. By applying ReckDroid to over 1000 Android apps in the wild, we find that apps with red packets account for 17.6% of apps from seven app markets (including Google Play) while red packet fraud mainly occurs in Chinese app markets.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
期刊介绍: Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.
期刊最新文献
A survey on privacy and security issues in IoT-based environments: Technologies, protection measures and future directions Practically implementing an LLM-supported collaborative vulnerability remediation process: A team-based approach An enhanced Deep-Learning empowered Threat-Hunting Framework for software-defined Internet of Things Editorial Board ReckDroid: Detecting red packet fraud in Android apps
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1