• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2964-2983
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• 2015

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.202-209
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• 2023

Android malware is now on the rise, because of the rising interest in the Android operating system. Machine learning models may be used to classify unknown Android malware utilizing characteristics gathered from the dynamic and static analysis of an Android applications. Anti-virus software simply searches for the signs of the virus instance in a specific programme to detect it while scanning. Anti-virus software that competes with it keeps these in large databases and examines each file for all existing virus and malware signatures. The proposed model aims to provide a machine learning method that depend on the malware detection method for Android inability to detect malware apps and improve phone users' security and privacy. This system tracks numerous permission-based characteristics and events collected from Android apps and analyses them using a classifier model to determine whether the program is good ware or malware. This method used the machine learning techniques KNN-SVM, DBN, and GRU in which help to find the accuracy which gives the different values like KNN gives 87.20 percents accuracy, SVM gives 91.40 accuracy, Naive Bayes gives 85.10 and DBN-GRU Gives 97.90. Furthermore, in this paper, we simply employ standard machine learning techniques; but, in future work, we will attempt to improve those machine learning algorithms in order to develop a better detection algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3420-3436
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• 2020

Due to the increasing number of malicious software (also known as malware), methods for sharing threat information are being studied by various organizations. The Malware Attribute Enumeration and Characterization (MAEC) format of malware is created by analysts, converted to Structured Threat Information Expression (STIX), and distributed by using Trusted Automated eXchange of Indicator Information (TAXII) protocol. Currently, when sharing malware analysis results, analysts have to manually input them into MAEC. Not many analysis results are shared publicly. In this paper, we propose an automated MAEC conversion technique for sharing analysis results of malicious Android applications. Upon continuous research and study of various static and dynamic analysis techniques of Android Applications, we developed a conversion tool by classifying parts that can be converted automatically through MAEC standard analysis, and parts that can be entered manually by analysts. Also using MAEC-to-STIX conversion, we have discovered that the MAEC file can be converted into STIX. Although other researches have been conducted on automatic conversion techniques of MAEC, they were limited to Windows and Linux only. In further verification of the conversion rate, we confirmed that analysts could improve the efficiency of analysis and establish a faster sharing system to cope with various Android malware using our proposed technique.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제6권3호
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• pp.159-168
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• 2017

스마트폰 사용자가 증가하고 스마트폰이 다양한 서비스와 함께 일상생활에서 널리 사용됨에 따라 스마트폰 사용자를 노리는 악성코드 또한 증가하고 있다. 안드로이드는 2012년 이후로 가장 많이 사용되고 있는 스마트폰 운영체제이지만, 안드로이드 마켓의 개방성으로 인해 수많은 악성 앱이 마켓에 존재하며 사용자에게 위협이 되고 있다. 현재 대부분의 안드로이드 악성 앱 탐지 프로그램이 사용하는 규칙 기반의 탐지 방법은 쉽게 우회가 가능할 뿐만 아니라, 새로운 악성 앱에 대해서는 대응이 어렵다는 문제가 존재한다. 본 논문에서는 앱의 정적 분석과 딥러닝을 결합하여 스마트폰에서 직접 악성 앱을 탐지할 수 있는 방법을 제안한다. 수집한 6,120개의 악성 앱과 7,000개의 정상 앱 데이터 셋을 가지고 제안하는 방법을 평가한 결과 98.05%의 정확도로 악성 앱과 정상 앱을 분류하였고, 학습하지 않은 악성 앱 패밀리의 탐지에서도 좋은 성능을 보였으며, 스마트폰 환경에서 평균 10초 내외로 분석을 수행하였다.

• International journal of advanced smart convergence
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• 제11권3호
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• pp.23-27
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• 2022

In this paper, we explore a new type of Android evasive malware based on the Sequential Probability Ratio Test (SPRT) that does not perform malicious task when it discerns that dynamic analyzer is input generator. More specifically, a new type of Android evasive malware leverages the intuition that dynamic analyzer provides as many inputs within a certain amount of time as possible to Android apps to be tested, while human users generally provide necessary inputs to Android apps to be used. Under this intuition, it harnesses the SPRT to discern whether dynamic analyzer runs in Android system or not in such a way that the number of inputs per time slot exceeding a preset threshold is regarded as evidence that inputs are provided by dynamic analyzer, expediting the SPRT to decide that dynamic analyzer operates in Android system and evasive malware does not carry out malicious task.

• 정보보호학회논문지
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• 제29권3호
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• pp.519-529
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• 2019

지능화된 안드로이드 악성코드는 안티바이러스가 탐지하기 어렵도록 악성행위를 숨기기 위하여 다양한 분석 회피 기법을 적용하고 있다. 악성코드는 악성행위를 숨기기 위하여 백그라운드에서 동작하는 컴포넌트를 주로 활용하고, 자동화된 스크립트로 악성 앱을 실행할 수 없도록 activity-alias 기능으로 실행을 방해하고, 악성행위가 발견되는 것을 막기 위해 logcat의 로그를 삭제하는 등 지능화되어간다. 악성코드의 숨겨진 컴포넌트는 기존 정적 분석 도구로 추출하기 어려우며, 기존 동적 분석을 통한 연구는 컴포넌트를 일부만 실행하기 때문에 분석 결과를 충분히 제공하지 못한다는 문제점을 지닌다. 본 논문에서는 이러한 지능화된 악성코드의 동적 분석 성공률을 증가시키기 위한 시스템을 설계하고 구현하였다. 제안하는 분석 시스템은 악성코드에서 숨겨진 컴포넌트를 추출하고, 서비스와 같은 백그라운드 컴포넌트인 실행시키며, 앱의 모든 인텐트 이벤트를 브로드캐스트한다. 또한, 분석 시스템의 로그를 앱이 삭제할 수 없도록 logcat을 수정하고 이를 이용한 로깅 시스템을 구현하였다. 실험 결과 본 논문에서 제안한 시스템을 기존의 컨테이너 기반 동적 분석 플랫폼과 비교하였을 때, 악성코드 구동률이 70.9%에서 89.6%로 향상된 기능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.959-981
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• 2017

Existing Android malware detection approaches mostly have concentrated on superficial features such as requested or used permissions, which can't reflect the essential differences between benign apps and malware. In this paper, we propose a quantitative calculation model of application risks based on the key observation that the essential differences between benign apps and malware actually lie in the way how permissions are used, or rather the way how their corresponding permission methods are used. Specifically, we employ a fine-grained analysis on Android application risks. We firstly classify application risks into five specific categories and then introduce comprehensive risk, which is computed based on the former five, to describe the overall risk of an application. Given that users' risk preference and risk-bearing ability are naturally fuzzy, we design and implement a fuzzy logic system to calculate the comprehensive risk. On the basis of the quantitative calculation model, we propose a risk classification based approach for Android malware detection. The experiments show that our approach can achieve high accuracy with a low false positive rate using the RandomForest algorithm.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.374-376
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• 2022

악성 앱 분석 도구는 안드로이드 기반 앱을 도구에 정의된 AI 기반 알고리즘에 의해 분석되어 악성코드가 포함되었는지 탐지하는 시스템이다. 현재 스마트폰의 보급이 활성화됨에 따라 악성 앱을 사용한 범죄가 증가하였고, 이에 따라 악성 앱에 대한 보안이 요구되는 실정이다. 스마트폰에 사용되는 안드로이드 운영체제는 점유율이 70%이상이며, 오픈소스 기반이기 때문에 많은 취약성 및 악성코드가 존재할뿐만 아니라, 악성 앱에 대한 피해도 증가하여 악성 앱을 탐지하고 분석하는 도구에 대한 수요도 증가할 것이다. 하지만, 악성 앱 분석 도구에 대한 보안기능요구사항이 정확히 명시되지 않아, 악성 앱 분석도구를 구축 및 개발하는데 있어 많은 어려움이 있기 때문에 본 논문을 제안한다. 개발한 보호프로파일을 통해 악성 앱 분석 도구의 설계 및 개발에 기반이 되어 기술력을 향상시킬 수 있고, 악성 앱에 대한 피해를 최소화하여 안전성을 확보 할 수 있으며, 더 나아가 정보보호제품 인증을 통해 악성 앱 분석 도구에 대한 신뢰를 보증할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3654-3670
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• 2019

This paper proposes an Image Texture Median Filter (ITMF) to analyze and detect Android malware on Drebin datasets. We design a model of "ITMF" combined with Image Processing of Median Filter (MF) to reflect the similarity of the malware binary file block. At the same time, using the MAEVS (Malware Activity Embedding in Vector Space) to reflect the potential dynamic activity of malware. In order to ensure the improvement of the classification accuracy, the above-mentioned features(ITMF feature and MAEVS feature)are studied to train Restricted Boltzmann Machine (RBM) and Back Propagation (BP). The experimental results show that the model has an average accuracy rate of 95.43% with few false alarms. to Android malicious code, which is significantly higher than 95.2% of without ITMF, 93.8% of shallow machine learning model SVM, 94.8% of KNN, 94.6% of ANN.