• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.455-457
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• 2021

App is used on mobile devices such as smartphones and also has malicious code, which can be divided into normal and malicious depending on the presence or absence of hacking codes. Because there are many kind of malware, it is difficult to detect directly, we propose a method to detect malicious app using AI. Most of the existing methods are to detect malicious app by extracting features from malicious app. However, the number of types have increased exponentially, making it impossible to detect malicious code. Therefore, we would like to propose two more methods besides detecting malicious app by extracting features from most existing malicious app. The first method is to learn normal app to extract normal's features, as opposed to the existing method of learning malicious app and find abnormalities (malicious app). The second one is an 'ensemble technique' that combines the existing method with the first proposal. These two methods need to be studied so that they can be used in future mobile environment.

• 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.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.217-221
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• 2021

Recent studies have indicated that mobile markets harbor applications (apps) that are either malicious or vulnerable, compromising millions of devices. Some studies indicate that 96% of companies' employees have used at least one malicious app. Some app stores do not employ security quality attributes regarding authorization, which is the function of specifying access rights to access control resources. However, well-defined access control policies can prevent mobile apps from being malicious. The problem is that those who oversee app market sites lack the mechanisms necessary to assess mobile app security. Because thousands of apps are constantly being added to or updated on mobile app market sites, these security testing mechanisms must be automated. This paper, therefore, introduces a new mechanism for testing mobile app security, using white-box testing in a way that is compatible with Bring Your Own Device (BYOD) working environments. This framework will benefit end-users, organizations that oversee app markets, and employers who implement the BYOD trend.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.11-24
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• 2019

Using cross-platform development frameworks, mobile app developers can easily implement mobile apps for multiple platforms in one step. The frameworks also provides adversaries with the ability to write malicious code once, and then run it anywhere for other platforms. In this paper, we analyze the ratio of benign and malicious apps written by cross-platform development frameworks for Android apps collected from AndroZoo's site. The analysis results show that the percentage of benign apps written in the frameworks continues to increase, accounting for 45% of all benign apps in 2018. The percentage of malicious apps written in the frameworks accounted for 25% of all malicious apps in 2015, but that percentage has declined since then. This study provides useful information to make a suitable choice when app developers face several challenges in cross platform app development.

• Journal of KIISE
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• v.41 no.9
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• pp.707-714
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• 2014

In general, the benign apps transmit privacy information to the external to provide service to users as the malicious app does. In other words, the behavior of benign apps is similar to the one of malicious apps. Thus, the benign app can be easily manipulated for malicious purposes. Therefore, the malicious apps as well as the benign apps should notify the users of the possibility of privacy information leakage before installation to prevent the potential malicious behavior. In this paper, We propose the method to detect leakage of privacy information on the android app by analyzing API inter-dependency and shortest distance. Also, we present LeakDroid which detects leakage of privacy information on Android with the above method. Unlike dynamic approaches, LeakDroid analyzes Android apps on market site. To verify the privacy information leakage detection of LeakDroid, we experimented the well-known 250 malicious apps and the 1700 benign apps collected from Android Third party market. Our evaluation result shows that LeakDroid reached detection rate of 96.4% in the malicious apps and detected 68 true privacy information leakages inside the 1700 benign apps.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.11-18
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• 2016

Due to open source policy, Android systems are exposed to a variety of security problems. In particular, app reuse attacks are detrimental threat to the Android system security. This is because attacker can create core malign components and quickly generate a bunch of malicious apps by reusing these components. Hence, it is very imperative to discern whether Android apps contain reused components. To meet this need, we propose an Android app reuse analysis technique based on the Sequential Hypothesis Testing. This technique quickly makes a decision with a few number of samples whether a set of Android apps is made through app reuse. We performed experimental study with 6 malicious app groups, 1 google and 1 third-party app group such that each group consists of 100 Android apps. Experimental results demonstrate that our proposed analysis technique efficiently judges Android app groups with reused components.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.25-36
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• 2017

As smartphone users have increased in recent years, various applications have been developed and used especially for Android-based mobile devices. However, malicious applications developed by attackers for malicious purposes are also distributed through 3rd party open markets, and damage such as leakage of personal information or financial information of users in mobile terminals is continuously increasing. Therefore, to prevent this, a method is needed to distinguish malicious apps from normal apps for Android-based mobile terminal users. In this paper, we analyze the existing researches that detect malicious apps by extracting the system call events that occur when the app is executed. Based on this, we propose a technique to identify malicious apps by analyzing the sequence similarity of kernel layer events occurring in the process of running an app on commercial Android mobile devices.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.125-139
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• 2013

Distribution of malicious applications developed by attackers is increasing along with general normal applications due to the openness of the Android-based open market. Mechanism that allows more accurate ways to distinguish normal apps and malicious apps for common mobile devices should be developed in order to reduce the damage caused by the rampant malicious applications. This paper analysed the normal event pattern from the most highly used game apps in the Android open market to analyse the event pattern from normal apps and malicious apps of mobile devices that are based on the Android platform, and analysed the malicious event pattern from the malicious apps and the disguising malicious apps in the form of a game app among 1260 malware samples distributed by Android MalGenome Project. As described, experiment that extracts normal app and malicious app events was performed using Strace, the Linux-based system call extraction tool, targeting normal apps and malicious apps on Android-based mobile devices. Relevance analysis for each event set was performed on collected events that occurred when normal apps and malicious apps were running. This paper successfully extracted event similarity through this process of analyzing the event occurrence characteristics, pattern and distribution on each set of normal apps and malicious apps, and lastly suggested a mechanism that determines whether any given app is malicious.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.617-623
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• 2018

This study focuses on detection of malicious code through AndroidManifest permissoion feature extracted based on Android static analysis. Features are built on the permissions of AndroidManifest, which can save resources and time for analysis. Malicious app detection model consisted of SVM (support vector machine), NB (Naive Bayes), Gradient Boosting Classifier (GBC) and Logistic Regression model which learned 1,500 normal apps and 500 malicious apps and 98% detection rate. In addition, malicious app family identification is implemented by multi-classifiers model using algorithm SVM, GPC (Gaussian Process Classifier) and GBC (Gradient Boosting Classifier). The learned family identification machine learning model identified 92% of malicious app families.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.