• Convergence Security Journal
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• v.23 no.2
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• pp.27-36
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• 2023

In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.851-865
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• 2017

Ransomware such as WannaCry is a global issue and methods to defend against malware attacks are important. We have to be able to classify the malware types efficiently in order to minimize the damage from malwares. This study makes models to classify malware properly with various statistical techniques. Several classification techniques such as logistic regression, random forest, gradient boosting, and support vector machine are used to construct models. This study also helps us understand key variables to classify the type of malicious software.

• Review of KIISC
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• v.28 no.3
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• pp.11-17
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• 2018

세계적 열풍의 중심인 비트코인에는 많은 이슈가 발생하고 있다. 특히 비트코인의 익명성은 사회적으로 중요한 문제이다. 비트코인이 익명성을 보장하지 못할 경우 거래내역이 공개되어 프라이버시가 노출될 수 있다. 반대로 비트코인이 익명성을 보장할 경우 마약 거래, 자금 세탁, 랜섬웨어 공격 등의 각종 범죄가 발생할 수 있다. 이밖에도 다양한 상황에 적절한 대처를 하기 위해서는 비트코인 기술에 대한 정리와 이해가 필요하다. 본 논문에서는 비트코인의 익명성을 약화시키는 클러스터링 기술과, 비트코인의 익명성을 강화시키는 믹싱 프로토콜 기술에 대한 연구 흐름을 정리하였다.

• Information and Communications Magazine
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• v.34 no.3
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• pp.15-22
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• 2017

핀테크 시대가 본격화 되면서 결제/송금, 인터넷전문은행, 클라우드 펀딩 등 모든 분야의 핀테크 서비스는 금융 소비자의 개인 정보를 활용해야 하는 비대면 거래로 이뤄지는 만큼 보안 대책은 필수적이다. 핀테크에서 보안의 중요성은 아무리 강조해 다 지나치지 않는다. 금융사고 발생시 기업의 브랜드 가치는 물론 기업의 존폐 위기 까지 발생한다. 하루에도 악성코드가 수백만개씩 발생하고 있는 상황에서 금전적 이득을 노리는 피싱/파밍/스미싱/랜섬웨어 등 보안 위협은 날로 조직화되고 지능화되고 있다. 이렇게 지능화되고 조직화되고 있는 보안 위협으로 부터 소비자를 보호하고 안정적인 서비스를 제공하기 위해서는 보안 활동에 대해 알아본다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.153-154
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• 2018

전 세계 모바일 게임 소비 시장의 증가와 사용자들이 지속적으로 증가하는 반면 랜섬웨어와 같은 악성 프로그램들이 악의적인 목적을 위하여 모바일게임 시장에 피해를 주는 사례들도 지속적으로 증가하는 것도 사실이다. 본 논문에서는 모바일 게임을 이용한 악성코드 위협으로부터 보호하기 위하여 4차 산업의 가장 핵심 기술인 인공지능의 학습기술에 악성코드 분석기술을 연계시켜 새로운 모바일 악성코드 탐지와 속도를 향상시키는 기술의 필요성을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.258-259
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• 2020

한국인터넷진흥원에서는 2020년 사이버 공격에 대한 7대 전망을 일상 속 보안 취약점, 공공기관·기업 대상 랜섬웨어, 가상동화 거래소를 통한 해킹 사고, 문자 메시지·이메일을 통한 악성코드 감염, 지능형 표적 공격, 소프트웨어 공급망 공격, 융합 서비스 보안 위협으로 제시하였다. 이에 본 논문에서는 신규 사이버위협에 대한 동향 분석을 통하여 기관의 정보보안을 위해 대응할 수 있는 방안에 대해 살펴보고자 한다.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.126-132
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• 2022

At present, due to the rapid spread of smartphones and activation of IoT, malicious codes are disseminated using SNS, or intelligent intrusions such as intelligent APT and ransomware are in progress. The damage caused by the intelligent intrusion is also becoming more consequential, threatening, and emergent than the previous intrusion. Therefore, in this paper, we propose an intelligent intrusion situation-aware reasoning system to detect transgression behavior made by such intelligent malicious code. The proposed system was used to detect and respond to various intelligent intrusions at an early stage. The anticipated system is composed of an event monitor, event manager, situation manager, response manager, and database, and through close interaction between each component, it identifies the previously recognized intrusive behavior and learns about the new invasive activities. It was detected through the function to improve the performance of the inference device. In addition, it was found that the proposed system detects and responds to intelligent intrusions through the state of detecting ransomware, which is an intelligent intrusion type.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.39-46
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• 2021

Damages by malware have recently been increasing. Conventional signature-based antivirus solutions are helplessly vulnerable to unprecedented new threats such as Zero-day attack and ransomware. Despite that, many enterprises have retained signature-based antivirus solutions as part of the multiple endpoints security strategy. They do recognize the problem. This paper proposes a solution using the blockchain and deep learning technologies as the next-generation antivirus solution. It uses the antivirus software that updates through an existing DB server to supplement the detection unit and organizes the blockchain instead of the DB for deep learning using various samples and forms to increase the detection rate of new malware and falsified malware.

• Journal of Convergence for Information Technology
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• v.9 no.1
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• pp.142-150
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• 2019

This study is a file management security system that encrypts and decrypts computer and cloud data by using Bluetooth based cryptographic module. It is a necessary solution in terms of abuse of personal information and protection of social and national information. We developed H/W and S/W for SFMS(: Safe File Management Security) related Bluetooth module in cloud environment and implemented firmware development, encryption key generation and issuance, client program for system mobile and key management system. In the terminal internal encryption and decryption, SFMS was developed to ensure high security that the hacking itself is not possible because key values exist separately for each file.