• 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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• 2018.10a
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• pp.306-308
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• 2018

This paper proposes a variant malicious code detection algorithm in a mobile environment using a deep learning algorithm. In order to solve the problem of malicious code detection method based on Android, we have proved high detection rate through signature based malicious code detection method and realtime malicious file detection algorithm using machine learning method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.519-529
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• 2019

Nowadays, intelligent Android malware applies anti-analysis techniques to hide malicious behaviors and make it difficult for anti-virus vendors to detect its presence. Malware can use background components to hide harmful operations, use activity-alias to get around with automation script, or wipe the logcat to avoid forensics. During our study, several static analysis tools can not extract these hidden components like main activity, and dynamic analysis tools also have problem with code coverage due to partial execution of android malware. In this paper, we design and implement a system to analyze intelligent malware that uses anti-analysis techniques to improve detection rate of evasive malware. It extracts the hidden components of malware, runs background components like service, and generates all the intent events defined in the app. We also implemented a real-time logging system that uses modified logcat to block deleting logs from malware. As a result, we improve detection rate from 70.9% to 89.6% comparing other container based dynamic analysis platform with proposed system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.17-30
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• 2015

Unlike existing widely used bytecode-centric Android application code obfuscation methodology, our scheme in this paper makes encrypted file i.e. DEX file self-extracted arbitrary Android application. And then suggests a method regarding making the loader app to execute encrypted file's code after saving the file in arbitrary folder. Encrypted DEX file in the loader app includes original code and some of Manifest information to conceal event treatment information. Loader app's Manifest has original app's Manifest information except included information at encrypted DEX. Using our scheme, an attacker can make malicious code including obfuscated code to avoid anti-virus software at first. Secondly, Software developer can make an application with hidden main algorithm to protect copyright using suggestion technology. We implement prototype in Android 4.4.2(Kitkat) and check obfuscation capacity of malicious code at VirusTotal to show effectiveness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.127-130
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• 2013

Apple's iPhone was released and the SmartPhone craze started in the world. Then, Many kind of SmartPhone released on the market, the number of subscribers increased explosively for the convenience of SmartPhone. By SmartPhone users increases rapidly, the number of Malware targeting SmartPhones increased explosively. The SmartPhone Malware, tend to increase explosively in 2012 beginning to be discovered in earnest from the second half of 2011, and is continuously increasing even now. In this paper, describes the status and trends of SmartPhone Malware, through the analysis of trends in the SmartPhone Malware, we describe the future prospects of SmartPhone Malware.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.19-26
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• 2021

The performance of deep learning-based malware detection and classification models depends largely on how to construct a feature set to be applied to training. In this paper, we propose an approach to select the optimal feature set to maximize detection performance for CNN-based Android malware detection. The features to be included in the feature set were selected through the Chi-Square test algorithm, which is widely used for feature selection in machine learning and deep learning. To validate the proposed approach, the CNN model was trained using 36 characteristics selected for the CICANDMAL2017 dataset and then the malware detection performance was measured. As a result, 99.99% of Accuracy was achieved in binary classification and 98.55% in multiclass classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.395-397
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• 2014

국내에서 가장 폭넓게 사용되는 모바일 운영체제인 안드로이드는 수 많은 악성코드에 대한 위협 속에 있다. 그 중에서 가장 위협적인 공격은 루트 권한을 획득하는 악성코드이다. 따라서 본 연구는 가상화 환경을 통해 안드로이드 시스템에서 실존하는 루트 권한 획득을 탐지하는 시스템을 소개 하고 있다. 이를 위해 CPU 제조사에서 제공하는 가상화 기반 기술을 활용하였으며 결과적으로 시스템 상에서 루트 권한으로 동작하는 프로세스를 감지할 수 있었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.223-226
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• 2013

Information security breaches caused by malicious code is arising in various forms with exponential growth. The latest information security threats on computers are increasing, especially on smartphone, which has enabled malicious code to quickly surge. As a result, the leakage of personal information, such as billing information, is under threat. Meanwhile the attack vector o smartphone malware is difficult to detect. In this paper, we propose a smartphone security system to respond to the spread of malicious code by iPhone and Android OS-based malware analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.781-784
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• 2012

스마트폰의 사용이 늘어나면서 스마트폰의 취약점을 노리는 악성코드들도 많이 발생되고 있다. 특히 악성코드를 숨겨주는 루트킷이 최근 캐리어IQ사태를 통해 이슈가 되면서 루트킷에 대한 관심이 늘어가고 있다. 루트킷은 동작방식에 따라 유저모드 루트킷과 커널모드 루트킷으로 나눌 수 있는데 PC처럼 운영체제를 통해 자원 및 프로세스를 제어하는 스마트폰도 루트킷의 위협에 안전하지 못하다. 본 논문은 PC환경에서 동작하는 루트킷의 동작원리를 파악하고 스마트폰 환경 특히 안드로이드 환경의 유저모드 루트킷의 동작과 위협을 주지시키고자 한다.