{"title":"ReckDroid: Detecting red packet fraud in Android apps","authors":"Yu Cheng , Xiaofang Qi , Yanhui Li , Yumeng Wang","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":"148 ","pages":"Article 104117"},"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}
引用次数: 0
Abstract
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.
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.