• Review of KIISC
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• v.34 no.2
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• pp.57-62
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• 2024

전 세계적으로 막대한 피해를 주고 있는 랜섬웨어는 다양한 방법으로 주요 조직을 대상으로 표적 공격을 펼친다. 랜섬웨어는 일반적으로 공격 대상 시스템의 파일을 암호화하기 위해 블록 암호, 스트림 암호와 같은 대칭키 암호를 사용하고, 해당 파일을 암호화할 때 사용되었던 대칭키를 공개키 암호로 보호한다. 여기서, 대칭키를 암호화할 때 RSA, ECC와 같은 공개키 암호를 주로 사용하는데, 이는 현재 전 세계적으로 이슈가 되고 있는 양자 컴퓨터의 발전과 Shor 알고리즘을 이용하여 무력화할 수 있다. 이러한 이유로 랜섬웨어가 기존의 암호화 구성에서 공개키 암호를 대신하여 양자 후 암호를 적용한다면, 양자 컴퓨터를 통한 랜섬웨어 대응이 불가능해진다. 본 논문에서는 랜섬웨어의 최신 기술 동향을 분석하고, 랜섬웨어의 암호화 구성에 기존 공개키 암호 대신 양자 후 암호를 적용 시 발생하는 공격 대응 원리에 대해 설명한다.

• Review of KIISC
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• v.32 no.3
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• pp.41-47
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• 2022

랜섬웨어는 2005년부터 알려지면서 지금까지도 전 세계적으로 큰 피해를 입히며, 사회적으로 심각한 문제를 야기하고 있다. 또한, 랜섬웨어 공격 그룹은 개인보다는 금전적 이익을 크게 얻을 수 있는 기업들을 주로 공격하고 있으며, 이에 대응하기 위해 각 국에서는 랜섬웨어에 대한 대응하는 방법과 정보를 제공하고 있다. 따라서 본 고에서는 많은 비중을 차지하고 있는 블록암호 기반 랜섬웨어에 대한 분석 사례 동향을 살펴보고자 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.449-456
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• 2018

The advent of cryptography has become a means of obtaining real monetary benefits to hackers, which has recently led to a surge in the number of Ransomware and the associated damage has increased significantly. It is expected that malicious codes will be expanded to new areas by meeting passwords, and Ransomware will be further increased in the future. To solve these problems, we need a model that can detect and block intrusion of Ransomware by analyzing the intrusion path of Ransomware. In this paper, we collect and analyze the data of Ransomware, and create and analyze Ransomware's color Petri net model.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.139-145
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• 2018

Ransomeware is a kind of malware. Computers infected with Ransomware have limited system access. It is a malicious program that must provide a money to the malicious code maker in order to release it. On May 12, 2017, with the largest Ransomware attack ever, concerns about the Internet security environment are growing. The types of Ransomware and countermeasures to prevent cyber terrorism are discussed. Ransomware, which has a strong infectious nature and has been constantly attacked in recent years, is typically in the form of Locky, Petya, Cerber, Samam, and Jigsaw. As of now, Ransomware defense is not 100% free. However, it can counter to Ransomware through automatic updates, installation of vaccines, and periodic backups. There is a need to find a multi-layered approach to minimize the risk of reaching the network and the system. Learn how to prevent Ransomware from corporate and individual users.

• Review of KIISC
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• v.31 no.6
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• pp.5-12
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• 2021

랜섬웨어는 2021년 가장 주목해야 할 사이버 위협으로 여겨지며, 전 세계적으로 큰 피해를 입혔다. 특히 국가 핵심 인프라 시설과 기업을 대상으로 대규모 공격을 지속하였으며, 파일을 암호화하는 것 뿐만 아니라 기업의 기밀 정보를 유출함으로써 2차 피해 우려를 낳고 있다. 이에 따라 세계 각국에서는 랜섬웨어를 대응 및 예방하고자 다양한 지침을 발표하였다. 본 논문에서는 2021년 국내·외에서 발생한 랜섬웨어 사건·사고와 새롭게 등장한 랜섬웨어에 대해 알아보고, 국가별 랜섬웨어 대응 및 예방 정책에 관해 소개한다.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.497-505
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• 2018

Attacking computer systems using ransomware is very common all over the world. Since antivirus and detection methods are constantly improved in order to detect and mitigate ransomware, the ransomware itself becomes equally better to avoid detection. Several new methods are implemented and tested in order to optimize the protection against ransomware. In our work, 582 of ransomware and 942 of normalware sample data along with 30,967 dynamic action sequence variables are used to detect ransomware efficiently. Several variable selection techniques combined with various machine learning based classification techniques are tried to protect systems from ransomwares. Among various combinations, chi-square variable selection and random forest gives the best detection rates and accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.251-258
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• 2017

Most malware, including Ransomware, is built for the Windows operating system. This is because it is more harmful to target an operating system with a high share. But in recent years, MacOS's operating system share has steadily increased. As people become more and more used, the number of malicious code running on the MacOS operating system is increasing. Ransomware has been known to Korea since 2015, and damage cases are gradually increasing. MacOS is no longer free from Ransomware, as Ransomware for MacOS was discovered in March 2016. In order to cope with future Ransomware, this paper used Ransomware's modified file extension to detect Ransomware. We have studied how to detect and block Ransomware processes by distinguishing between extensions changed by the user and extensions changed by the Ransomware process.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.251-258
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• 2018

Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis.

• Journal of Industrial Convergence
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• v.21 no.9
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• pp.41-48
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• 2023

Recently, there has been a sharp increase in the damages caused by ransomware across various sectors of society, including individuals, businesses, and nations. Ransomware is a malicious software that infiltrates user computer systems, encrypts important files, and demands a ransom in exchange for restoring access to the files. Due to its diverse and sophisticated attack techniques, ransomware is more challenging to detect than other types of malware, and its impact is significant. Therefore, there is a critical need for accurate detection and mitigation methods. To achieve precise ransomware detection, an inference engine of a detection system must possess knowledge of ransomware features. In this paper, we propose a model to extract and classify the characteristics of ransomware for accurate detection of ransomware, calculate the similarity of the extracted characteristics, reduce the dimension of the characteristics, group the reduced characteristics, and classify the characteristics of ransomware into attack tools, inflow paths, installation files, command and control, executable files, acquisition rights, circumvention techniques, collected information, leakage techniques, and state changes of the target system. The classified characteristics were applied to the existing ransomware to prove the validity of the classification, and later, if the inference engine learned using this classification technique is installed in the detection system, most of the newly emerging and variant ransomware can be detected.