• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.947-952
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• 2012

The technology suggested in this study is the method to contain executable software in a portable storage devices like the USB and install an application program to operate it in any client devices. To solve this problem, we can store pre-scanned information on the above client's files, registry, and services in the device before installing the proper application program to be installed using the scanning method and manage in the USB the resources that generated and changed by post-scanning the information after installing the above application program in the client's device. After that, necessary files in the client to be used can be copied for using, and after use, related files are deleted, and it also includes the process to return to the previous system environment. This method is advantageous in that once any sort of application programs needed to be installed gets to be installed in an external portable storage devices like the USB, the application program installed can be operated in any computer not raising any issues like illegal copy.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.103-106
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• 2023

코로나 팬데믹 사태로 인해 업무환경이 재택근무를 하는 환경으로 바뀌고 악성코드의 변종 또한 빠르게 발전하고 있다. 악성코드를 분석하고 백신 프로그램을 만들면 새로운 변종 악성코드가 생기고 변종에 대한 백신프로그램이 만들어 질 때까지 변종된 악성코드는 사용자에게 위협이 된다. 본 연구에서는 머신러닝 알고리즘을 사용하여 악성파일 여부를 예측하는 방법을 제시하였다. 일반적인 악성코드의 구조를 갖는 Portable Executable 구조 파일을 파이썬의 LIEF 라이브러리를 사용하여 Certificate, Imports, Opcode 등 3가지 feature에 대해 정적분석을 하였다. 학습 데이터로는 정상파일 320개와 악성파일 530개를 사용하였다. Certificate는 hasSignature(디지털 서명정보), isValidcertificate(디지털 서명의 유효성), isNotExpired(인증서의 유효성)의 feature set을 사용하고, Imports는 Import Address Table의 function 빈도수를 비교하여 feature set을 구축하였다. Opcode는 tri-gram으로 추출하여 빈도수를 비교하여 feature set을 구축하였다. 테스트 데이터로는 정상파일 360개 악성파일 610개를 사용하였으며 Feature set을 사용하여 random forest, decision tree, bagging, adaboost 등 4가지 머신러닝 알고리즘을 대상으로 성능을 비교하였고, bagging 알고리즘에서 약 0.98의 정확도를 보였다.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.87-93
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• 2022

Since 99% of PCs operating in the defense domain use the Windows operating system, detection and response of Window-based malware is very important to keep the defense cyberspace safe. This paper proposes a model capable of detecting malware in a Windows PE (Portable Executable) format. The detection model was designed with an emphasis on rapid update of the training model to efficiently cope with rapidly increasing malware rather than the detection accuracy. Therefore, in order to improve the training speed, the detection model was designed based on a Bidirectional LSTM (Long Short Term Memory) network that can detect malware with minimal sequence data without complicated pre-processing. The experiment was conducted using the EMBER2018 dataset, As a result of training the model with feature sets consisting of three type of sequence data(Byte-Entropy Histogram, Byte Histogram, and String Distribution), accuracy of 90.79% was achieved. Meanwhile, it was confirmed that the training time was shortened to 1/4 compared to the existing detection model, enabling rapid update of the detection model to respond to new types of malware on the surge.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.1
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• pp.43-50
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• 2013

File identification and analysis is an important process of computer forensics, since the process determines which subjects are necessary to be collected and analyzed as digital evidence. An efficient file classification aids in the file identification, especially in case of copyright infringement where we often have huge amounts of files. A lot of file classification methods have been proposed by far, but they have mostly focused on classifying malicious behaviors based on known information. In copyright infringement cases, we need a different approach since our subject includes not only malicious codes, but also vast number of normal files. In this paper, we propose an efficient file classification method that relies on undocumented information in the header of the PE format files. Out method is useful in copyright infringement cases, being applied to any sort of PE format executable file whether the file is malicious, packed, mutated, transformed, virtualized, obfuscated, or not.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.644-660
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• 2016

The real-time detection of malware remains an open issue, since most of the existing approaches for malware categorization focus on improving the accuracy rather than the detection time. Therefore, finding a proper balance between these two characteristics is very important, especially for such sensitive systems. In this paper, we present a fast portable executable (PE) malware detection system, which is based on the analysis of the set of Application Programming Interfaces (APIs) called by a program and some technical PE features (TPFs). We used an efficient feature selection method, which first selects the most relevant APIs and TPFs using the chi-square ($KHI^2$) measure, and then the Phi (${\varphi}$) coefficient was used to classify the features in different subsets, based on their relevance. We evaluated our method using different classifiers trained on different combinations of feature subsets. We obtained very satisfying results with more than 98% accuracy. Our system is adequate for real-time detection since it is able to categorize a file (Malware or Benign) in 0.09 seconds.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.117-120
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• 2004

본 고에서는 악성 프로그램을 탐지하기 위해 특정 프로그램 실행시 해당 파일 구조를 분석하여 악성 프로그램을 탐지하기 위해 Michael Weber, Matthew Schmid, Michaei Schatz와 David Geyer에 의해 제안된 실행코드 탐지 방식을 분석하고 있다. 제안된 방식에서는 기존 방법에서 사용하고 있는 악성 프로그램의 시그니처 분석을 통한 탐지 방법과 다르게 윈도우 PE 파일 형태의 파일 구조를 가지고 있는 실행 프로그램의 문맥 분석을 통해 알려지지 않은 악성 프로그램을 탐지함을 목적으로 하고 있다. 제안된 방식에서는 특히 PEAT(Portable Executable Analysis Toolkit)라는 이동 실행 분석 툴 킷을 개발, 사용함으로써 악성프로그램을 탐지 하고 있는데 이 툴 킷은 PE 파일 구조를 가진 임의의 애플리케이션에 대해 악성코드의 존재 여부를 밝힐 수 있는 실행시 구조적 특징을 이용한다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.546-551
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• 2007

소프트웨어 버스마크는 프로그램을 식별하는데 사용될 수 있는 프로그램의 고유한 특징을 말한다. 본 논문에서는 windows PE(Portable Executable) 파일의 API에 대한 정보를 가지는 임포트 테이블에 기반한 프로그램 버스마킹 기법을 제안한다. 버스마크의 신뢰도를 높이기 위한 방법으로 대부분의 Windows 프로그램에서 사용되는 범용의 API는 버스마크에서 제외시키고 프로그램 개개의 특성을 나타낼 수 있는 특화된 API에 초점을 맞추어서 비교하는 방법을 사용한다. 본 논문에서 제안한 버스마킹 기법을 평가하기 위해서 다양한 카테고리의 Windows 프로그램에 대해서 실험을 하였다. 신뢰도를 측정하기 위해서 같은 프로그램에 대해서 버전별로 비교를 하였고, 프로그램의 분류에 따라서 유사한 카테고리와 다른 카테고리에 대해서 비교를 하였다. 프로그램의 변환이나 난독화에도 견딜 수 있는 강인도(Resilience)를 평가하기 위해서 서로 다른 컴파일러를 사용하여 생성된 프로그램에 대해서 비교를 하였다. 실험 결과에서 본 논문에서 제안하는 버스마크가 프로그램의 특징을 충분히 표현하고 있음을 보여준다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1313-1322
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• 2016

Recently, many virus and hacking attacks on public organizations and financial institutions by internet are becoming increasingly intelligent and sophisticated. The Advanced Persistent Threat has been considered as an important cyber risk. This attack is basically accomplished by spreading malicious codes through complex networks. To detect and extract PE files in smart manufacturing industry networks, an efficient processing method which is performed before analysis procedure on malicious codes is proposed. We implement a preprocessor of open intrusion detection system Snort for fast extraction of PE files and install on a hardware sensor equipment. As a result of practical experiment, we verify that the network sensor can extract the PE files which are often suspected as a malware.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.563-577
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• 2017

Due to the Internet and computing capability, new and variant malware are discovered around 1 Million per day. Companies use dynamic analysis such as behavior analysis on virtual machines for unknown malware detection because attackers use unknown malware which is not detected by signature based AV effectively. But growing number of malware types are not only PE(Portable Executable) but also non-PE such as MS word or PDF therefore dynamic analysis must need more resources and computing powers to improve detection effectiveness. This study elicits the pre-filtering system evaluation factor to improve effective dynamic malware analysis system and presents and verifies the decision making model and the formula for solution selection using AHP(Analytics Hierarchy Process)