• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1108-1111
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• 2010

구글 안드로이드 플랫폼은 오픈소스 형태로 응용프로그램을 손쉽게 개발할 수 있는 환경을 제공하며 이러한 특징으로 인해 빠른 속도로 시장 점유율을 높이고 있다. 하지만 오픈 소스의 특징으로 인해 보안 취약점에 대한 우려가 증가하고 있다. 안드로이드 고유의 보안모델은 응용프로그램의 시스템자원에 대한 부적절한 접근을 제어하기 위한 권한을 중심으로 이루어진다. 본 연구에서는 안드로이드의 권한 기반 보안모델에 대한 취약성을 테스트 코드수행과 플랫폼 소스분석을 통해 알아보고 이에 대해 간단한 해결방안을 제시한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.