• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.895-902
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• 2016

Smart phone distribution rate has been rising and it's security threat also has been rising. Especially Android smart phone reaches nearly 85% of domestic share. Since repackaging on android smart phone is relatively easy, the number of re-packaged malwares has shown steady increase. While many detection techniques have been proposed in order to prevent malwares, it is not easy to detect re-packaged malwares by static analysis and it is also difficult to operate dynamic analysis in android smart phone. Static analysis proposed in this paper features code reuse of repackaged malwares. We extracted DEX files from android applications and performed static analysis using class names and method names. This process doesn't not include reverse engineering, so it is possible to detect malwares efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.61-68
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• 2019

Although the number of smartphone users is continuously increasing due to the advantage of being able to easily use most of the Internet services, the number of counterfeit applications is rapidly increasing and personal information stored in the smartphone is leaked to the outside. Because Android app was developed with Java language, it is relatively easy to create counterfeit apps if attacker performs the de-compilation process to reverse app by abusing the repackaging vulnerability. Although an obfuscation technique can be applied to prevent this, but most mobile apps are not adopted. Therefore, it is fundamentally impossible to block repackaging attacks on Android mobile apps. In addition, personal information stored in the smartphone is leaked outside because it does not provide a forgery self-verification procedure on installing an app in smartphone. In order to solve this problem, blockchain is used to implement a process of certificated application registration and a fake app identification and detection mechanism is proposed on Hyperledger Fabric framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.1-10
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• 2020

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.757-766
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• 2013

The Malicious code that targets Android is growing dramatically as the number of Android users are increasing. Most of the malicious code have an intention of leaking personal information. Recently in Korea, a malicious code 'chest' has appeared and generated monetary damages by using malicious code to leak personal information and try to make small purchases. A variety of techniques to detect personal information leaks have been proposed on Android platform. However, the existing techniques are hard to apply to the user's smart-phone due to the characteristics of Android security model. This paper proposed a technique that detects and blocks file approaches and internet connections that are not allowed access to personal information by using the system call hooking in the kernel and white-list based approach policy. In addition, this paper proved the possibility of a real application on smart-phone through the implementation.

• 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.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2051-2066
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• 2017

This paper deals with reconfigurable secured mobile systems where the reconfigurability has the potential of providing a required adaptability to change the system requirements. The reconfiguration scenario is presented as a run-time automatic operation which allows security mechanisms and the addition-removal-update of software tasks. In particular, there is a definite requirement for filtering and intrusion detection mechanisms that will use fewer resources and also that will improve the security on the secured mobile devices. Filtering methods are used to control incoming traffic and messages, whereas, detection methods are used to detect malware events. Nevertheless, when different reconfiguration scenarios are applied at run-time, new security threats will be emerged against those systems which need to support multiple security objectives: Confidentiality, integrity and availability. We propose in this paper a new approach that efficiently detects threats after reconfigurable scenarios and which is based on filtering and intrusion detection methods. The paper's contribution is applied to Android where the evaluation results demonstrate the effectiveness of the proposed middleware in order to detect the malicious events on reconfigurable secured mobile systems and the feasibility of running and executing such a system with the proposed solutions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1201-1215
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• 2015

Obfuscation tools can be used to protect android applications from reverse-engineering in android environment. However, obfuscation tools can also be misused to protect malicious applications. In order to evade detection of anti-virus, malware authors often apply obfuscation techniques to malicious applications. It is difficult to analyze the functionality of obfuscated malicious applications until it is deobfuscated. Therefore, a study on deobfuscation is certainly required to address the obfuscated malicious applications. In this paper, we analyze APKs which are obfuscated by commercial obfuscation tools and propose the deobfuscation method that can statically identify obfuscation options and deobfuscate it. Finally, we implement automatic identification and deobfuscation tool, then show the results of evaluation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.5
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• pp.1087-1097
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• 2017

As the penetration rate of smartphones increases, the number of malicious codes targeting smartphones is increasing. I 360 Security 's smartphone malware statistics show that malicious code increased 437 percent in the first quarter of 2016 compared to the fourth quarter of 2015. In particular, malicious applications, which are the main means of distributing malicious code on smartphones, are aimed at leakage of user information, data destruction, and money withdrawal. Often, it is operated by an API, which is an interface that allows you to control the functions provided by the operating system or programming language. In this paper, we propose a mechanism to detect malicious application based on the similarity of API pattern in normal application and malicious application by learning pattern of API in application derived from static analysis. In addition, we show a technique for improving the detection rate and detection rate for each label derived by using the corresponding mechanism for the sample data. In particular, in the case of the proposed mechanism, it is possible to detect when the API pattern of the new malicious application is similar to the previously learned patterns at a certain level. Future researches of various features of the application and applying them to this mechanism are expected to be able to detect new malicious applications of anti-malware system.