• Journal of IKEEE
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• v.26 no.3
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• pp.380-388
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• 2022

The traditional malware detection technologies collect known malicious programs and analyze their characteristics. Then such a detection technology makes a blacklist based on the analyzed malicious characteristics and checks programs in the user's system based on the blacklist to determine whether each program is malware. However, such an approach can detect known malicious programs, but responding to unknown or variant malware is challenging. In addition, since such detection technologies generally monitor all programs in the system in real-time, there is a disadvantage that they can degrade the system performance. In order to solve such problems, various methods have been proposed to analyze major behaviors of malicious programs and to respond to them. The main characteristic of ransomware is to access and encrypt the user's file. So, a new approach is to produce the whitelist of programs installed in the user's system and allow the only programs listed on the whitelist to access the user's files. However, although it applies such an approach, attackers can still perform malicious behavior by performing a DLL(Dynamic-Link Library) injection attack on a regular program registered on the whitelist. This paper proposes a method to respond effectively to attacks using DLL injection.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.234-237
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• 2017

정보화 사회가 도래함에 따라 정보를 가공, 처리 유통하는 활동이 주를 이루고 정보의 가치는 경제적 가치를 창출하는 요소로 연결됐다. 이와 맞물려 ICT(Information & Communication Technology) 산업이 발전함에 따라 정보를 디지털 데이터 형식으로 저장관리 한다. 이러한 이유로 디지털 정보를 노리는 악성 행위들이 디지털 세상에서 문제가 되고 있다. 그중 사용자의 동의 없이 컴퓨터에 불법으로 설치되어 사용자의 디지털 파일(정보)를 인질로 잡아 금전적인 요구를 하는 악성 프로그램인 랜섬웨어의 피해는 날로 증가하고 있다.[1]. 본 논문에서는 운영체제의 시스템 콜 후킹을 통한 읽기/쓰기 권한을 제한함으로써 다양한 종류의 랜섬웨어 중 파일 암호화 기반 랜섬웨어로부터 사용자가 선택적으로 파일을 보호할 수 있는 방안을 제시하려 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.374-376
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• 2022

The Malware App Analysis Tool is a system that analyzes Android-based apps by the AI-based algorithm defined in the tool and detects whether malware code is included. Currently, as the spred of smartphones is activated, crimes using malware apps have increased, and accordingly, security for malware apps is required. Android operating systems used in smartphones have a share of more than 70% and are open-source-based, so not only will there be many vulnerabilities and malware, but also more damage to malware apps, increasing demand for tools to detect and analyze malware apps. However, this paper is proposed because there are many difficulties in designing and developing a malware app analysis tool because the security functional requirements for the malware app analysis tool are not clearly specified. Through the developed protection profile, technology can be improved based on the design and development of malware app analysis tools, safety can be secured by minimizing damage to malware apps, and furthermore, trust in malware app analysis tools can be guaranted through common criteria.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.1-7
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• 2023

Cases in which personal terminals or servers are infected by ransomware are rapidly increasing. Ransomware uses a self-developed encryption module or combines existing symmetric key/public key encryption modules to illegally encrypt files stored in the victim system using a key known only to the attacker. Therefore, in order to decrypt it, it is necessary to know the value of the key used, and since the process of finding the decryption key takes a lot of time, financial costs are eventually paid. At this time, most of the ransomware malware is included in a hidden form in binary files, so when the program is executed, the user is infected with the malicious code without even knowing it. Therefore, in order to respond to ransomware attacks in the form of binary files, it is necessary to identify the encryption module used. Therefore, in this study, we developed a mechanism that can detect and identify by reverse analyzing the encryption module applied to the malicious code hidden in the binary file.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.73-81
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• 2021

When downloading files using an app or web-based application on the user's mobile phone, the path is set to be saved in the pre-defined default directory. Many applications requiring access to storage, including file managers, require a write or read permission of storage to provide numerous functions and services. This means that the application will have direct access to the download folder where the numerous files downloaded. In this paper, to prove our feasibility of attack using the security vulnerabilities mentioned above, we developed a file hacking function disguised as an encryption function in the file management application. The file that encrypted will be sent to hackers via E-mail simultaneously on the background. The developed application was evaluated from VirusTotal, a malicious analysis engine, was not detected as a malicious application in all 74 engines. Finally, in this paper, we propose a defense technique and an algorithm based on the Trusted Execution Environment (TEE) to supplement these storage vulnerabilities.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1365-1373
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• 2019

Meltdown and Spectre are vulnerabilities that exploit out-of-order execution and speculative execution techniques to read memory regions that are not accessible with user privileges. OS patches were released to prevent this attack, but older systems without appropriate patches are still vulnerable. Currently, there are some research to detect Meltdown and Spectre attacks, but most of them proposed dynamic analysis methods. Therefore, this paper proposes a binary signature that can be used to detect Meltdown and Spectre malware without executing them. For this, we collected 13 malicious codes from GitHub and performed binary pattern analysis. Based on this, we proposed a static detection method for Meltdown and Spectre malware. Our results showed that the method identified all the 19 attack files with 0.94% false positive rate when applied to 2,317 normal files.

• Review of KIISC
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• v.18 no.1
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• pp.70-77
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• 2008

디지털 포렌식에서 증거 데이터 분석의 효율성을 높이기 위해서는 잘 알려진 파일을 분석 대상에서 제외하거나, 특정 파일의 존재여부에 대한 검사가 필요하다. 이를 위하여, 시스템 파일, 폰트 파일, 응용 프로그램 파일 등 분석이 필요없는 파일 및 루트킷, 백도어, 익스플로잇 코드 등 악성 파일에 대한 해쉬 값을 미래 계산하여 저장해 둔 것을 소프트웨어 참조 데이터세트라고 한다. 이 논문에서는 소프트웨어 참조 데이터세트 구축에 대한 주요 동향에 대하여 살펴본다. 특히, 소프트웨어 참조 데이터세트 구축을 주도하고 있는 미국의 NSRL RDS에 대하여 활용가능성 측면에서 구체적으로 살펴본다. NSRL RDS에 대한 분석결과 실제 컴퓨터 포렌식 도구에서 활용하기 매우 어렵다는 사실을 알 수 있다.

• Review of KIISC
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• v.24 no.1
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• pp.39-44
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• 2014

악성코드는 다양해진 감염 경로를 통해 쉽게 노출될 수 있으며, 개인정보의 유출뿐만 아니라 봇넷을 이용한 DDoS 공격과 지능화된 APT 공격 등을 통해 심각한 보안 위협을 발생시키고 있다. 최근 악성코드들은 실행 후에는 메모리에서만 동작하는 방식으로 파일로 존재하지 않기 때문에 기존의 악성코드 탐지 기법으로 이를 찾기가 쉽지 않다. 이를 극복하고자 최근에는 물리 메모리 덤프를 포함하여 악성코드 분석 및 탐지 연구가 활발하게 진행되고 있다. 본 논문에서는 윈도우 시스템의 물리 메인 메모리에서 악성코드 탐지 기술에 대해 설명하고, 기존 개발된 물리 메모리 악성코드 탐지 도구에 대한 분석을 수행하여 도구별 악성코드 탐지 기능에 대한 특징을 설명한다. 물리 메모리 악성코드 탐지 도구의 분석 결과를 통해 기존 물리 메모리 악성코드 탐지 기술의 한계점을 제시하고, 향후 정확하고 효율적인 물리 메모리 악성코드 탐지의 기반 연구로 활용하고자 한다.

• 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.