• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.770-773
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• 2012

특정 기업 및 국가를 대상으로 하는 APT(Advanced Persistent Threat)공격의 경우 특정 시스템을 겨냥하여 제작되기 때문에 기존의 시그니처 기반의 악성코드 탐지 방식으로는 해당 악성코드를 탐지할 수 없다. 따라서 알려지지 않은 악성코드를 탐지할 수 있는 행위 기반의 악성코드 탐지 방식이 최근 이슈화되었다. 본 논문에서는 연구되고 있는 행위 분석 기반의 악성코드 탐지 방식들을 분석함으로써 향후 행위 기반 악성코드 탐지 기술 개발 및 연구에 기여하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.715-718
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• 2021

정보통신 기술이 발전함에 따라 악의적인 공격을 통해 보안문제를 발생시키고 있다. 또한 새로운 악성코드가 유포되어 기존의 시그니처 비교방식은 새롭게 발생하는 악성코드를 빠르게 분석 할 수 없다. 새로운 악성코드를 빠르게 분석하고 방어기법을 제안하기 위해 악성코드의 패밀리를 분류할 필요가 있다. 본 논문에서는 악성코드의 바이너리 파일을 이용해 시각화하고 CNN모델을 통해 분류한다. 또한 정확도를 높이기 위해 LBP, HOG를 통해 악성코드 이미지에서 중요한 특성을 찾고 데이터 클래스 불균형에서 오는 문제를 앙상블 모델을 통해 해결하는 시스템을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.501-503
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• 2015

Malicious(악의적인) + Code 즉, 악의적인코드를 포함한 소프트웨어라는 의미로 줄여 Malware(Malicious + Software) 라고 불리는 악성코드는 최근 네트워크와 컴퓨터의 급속한 발전에 따라 기하급수적으로 증가하고 있는 추세이다. 폭발적인 증가율 추세를 보이고 있는 악성코드의 위협을 대비하기 위해 악성코드에 대한 분석이 필요한데 그 분석의 종류로는 초기분석, 동적 분석, 정적분석으로 나누고 장, 단점을 정리하였다. 또한 악성코드 대량화에 따른 효율적인 분석과 빠른 의사결정을 위한 악성코드 유사도에 대한 연구를 소개하고 API Call Sequence와 분류된 API를 이용한 악성행위 유사도 판별법을 제시하고 실험하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.366-367
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• 2015

최근 악성코드의 유포 동향을 살펴보면 APT 공격이 많다. 본 논문에서는 악성코드 은닉사이트를 통하여 악성코드가 유포되는 과정과 그 과정에서 공격자가 침투하는 경로나 대표적인 취약점들에 대하여 설명하고 그에 대한 대응책에 대하여 논하고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.593-603
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• 2020

The advent of malicious code has increased exponentially due to the spread of malicious code generation tools in accordance with the development of the network, but there is a limit to the response through existing malicious code detection methods. According to this situation, a machine learning-based malicious code detection method is evolving, and in this paper, the feature of data is extracted from the PE header for machine-learning-based malicious code detection, and then it is used to automate the malware through autoencoder. Research on how to extract the indicated features and feature importance. In this paper, 549 features composed of information such as DLL/API that can be identified from PE files that are commonly used in malware analysis are extracted, and autoencoder is used through the extracted features to improve the performance of malware detection in machine learning. It was proved to be successful in providing excellent accuracy and reducing the processing time by 2 times by effectively extracting the features of the data by compressively storing the data. The test results have been shown to be useful for classifying malware groups, and in the future, a classifier such as SVM will be introduced to continue research for more accurate malware detection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.933-943
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• 2022

As the Internet develops and the utilization rate of computers increases, the threats posed by malware keep increasing. This leads to the demand for a system to automatically analyzes a large amount of malware. In this paper, an automatic malware analysis technique using a deep learning algorithm is introduced. Our proposed method uses CNN (Convolutional Neural Network) to analyze the malicious features represented as images. To reflect semantic information of malware for detection, our method uses the opcode frequency data of binary for image generation, rather than using bytes of binary. As a result of the experiments using the datasets consisting of 20,000 samples, it was found that the proposed method can detect malicious codes with 91% accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.533-536
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• 2012

인터넷의 급격한 성장과 함께 컴퓨터 통신 이용률이 폭발적으로 증가함에 따라 여러 악성코드가 등장하게 되었다. 이러한 악성코드는 시스템의 비정상 동작 유발, 네트워크 성능 저하, 개인정보유출의 문제를 발생시킨다. 현재의 악성코드 분석은 Signature 분석이 대부분이며, Signature 분석은 특정 패턴의 악성코드는 빠르게 탐지하나, 변조된 코드는 탐지하지 못하며, 이미 피해가 널리 퍼진 뒤 분석 및 차단이 가능하다는 단점을 가진다. 따라서 본 논문은 NDIS(Network Driver Interface Specification)를 이용하여 악성코드에 대해 수동적인 Signature 분석의 단점을 보완 하는 시스템 및 네트워크 상태 분석모델을 제시 하여 보다 능동적인 탐지 및 차단 프로세스를 정의하고, 모델 구현을 위한 방법을 제시한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.599-609
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• 2010

Korean government has recently abrogated WIPI policy to open domestic mobile phone market to the world, which may result in the influx of foreign smart phones. This circumstance has given users more wide range of choices to buy a product and also has brought benefit to buy mobile phone cheaply. On the other hands, this change might have brought potential danger of mobile malware incidents which have only occurred in foreign countries. There are standardized analysis methods and response guides for computer malwares, not but for mobile malwares in our country. In this paper, we introduce existing mobile malwares and available tools for their analysis. Considering domestic circumstances which might not be properly protected against mobile malwares, we propose analysis methods and response guide of mobile malwares.