• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.9-18
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• 2019

Android Application Package (APK) is vulnerable to repackaging attacks. Therefore, obfuscation technology was applied inside the Android APK file to cope with repackaging attack. However, as more advanced reverse engineering techniques continue to be developed, fake Android APK files to be released. A new approach is needed to solve this problem. A blockchain is a continuously growing list of records, called blocks, which are linked and secured using cryptography. Each block typically contains a cryptographic hash of theprevious block, a timestamp and transaction data. Once recorded, the data inany given block cannot be altered retroactively without the alteration of all subsequent blocks. Therefore, it is possible to check whether or not theAndroid Mobile APK is forged by applying the blockchain technology. In this paper, we construct a discrimination DApp (Decentralized Application) against forgery Android Mobile APK by recording and maintaining the legitimate APK in the consortium blockchain framework like Hyperledger Fabric by Composer. With proposed DApp, we can prevent the forgery and modification of the appfrom being installed on the user's Smartphone, and normal and legitimate apps will be widely used.

• Journal of the Korea Convergence Society
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• v.8 no.3
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• pp.49-61
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• 2017

Recently, convenience and usability are increasing with the development and deployment of various mobile applications on the Android platform. However, important information stored in the smartphone is leaked to the outside without knowing the user since the malicious mobile application is continuously increasing. A variety of mobile vaccines have been developed for the Android platform to detect malicious apps. Recently discovered server-based polymorphic(SSP) malicious mobile apps include obfuscation techniques. Therefore, it is not easy to detect existing mobile vaccines because some other form of malicious app is newly created by using SSP mechanism. In this paper, we analyze the correlation between the similarity of the method in the DEX file constituting the core malicious code and the permission similarity measure through APK de-compiling process for the SSP malicious app. According to the analysis results of DEX method similarity and permission similarity, we could extract the characteristics of SSP malicious apps and found the difference that can be distinguished from the normal app.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.17-24
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• 2023

Mobile apps frequently request permission to access sensitive data for user convenience. However, while using mobile applications, sensitive and personal data has been leaked even if users do not allow it. To deal with this problem, Google App Store has required developers to disclose how the mobile app handles user data in a privacy policy. However, users are not certain that the privacy policy describes all the app's behavior. They have no choice but to rely on the privacy policy to confirm how the app uses data. This study designed a system that checks the reliability of privacy policies by analyzing the privacy policy texts and mobile apps. First, the system extracts and analyzes the privacy policy texts to check which personal data the privacy policy discloses that the mobile apps can collect. After analyzing which data apps can access using android static analysis, we compare both results to analyze the reliability of privacy policies. For the experiment, we collected the APK files and metadata of about 13K android apps registered in the Google Play Store and preprocessed the apps by four conditions. According to the comparison between privacy policies and mobile app behavior, many apps can access more personal data than disclosed in the privacy policy.