• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.1226-1227
• /
• 2023

최근 그래프 기반 분석에 대한 연구가 활발히 진행되면서 이를 정보 보안 분야에 적용하려는 시도가 이루어지고 있다. 특히 GNN(Graph Neural Network)은 복잡한 네트워크 데이터를 모델링하고 관계를 분석하는 데 효과적이며, 악성 코드 탐지 등 사이버 공격에 대한 대응 능력을 향상시키는 데 활용할 수 있다. 하지만 GNN을 사용하기 위해서는 그래프의 노드가 될 IOC(Indicator of Compromise) 데이터가 필요하다. 본 논문에서는 IOC Extractor 중 하나인 Cyobstract를 통하여 위협 보고서로부터 IOC를 추출하는 방법과 이를 활용하여 그래프를 구축하고 분석할 방향을 제시한다.

• Convergence Security Journal
• /
• v.20 no.3
• /
• pp.21-28
• /
• 2020

In recent years, hacking and malware techniques have evolved and become sophisticated and complex, and numerous cyber-attacks are constantly occurring in various fields. Among them, the most widely used route for compromise incidents such as information leakage and system destruction was found to be E-Mails. In particular, it is still difficult to detect and identify E-Mail APT attacks that employ zero-day vulnerabilities and social engineering hacking techniques by detecting signatures and conducting dynamic analysis only. Thus, there has been an increased demand for indicators of compromise (IOC) to identify the causes of malicious activities and quickly respond to similar compromise incidents by sharing the information. In this study, we propose a method of extracting various forensic artifacts required for detecting and investigating Hacking E-Mails, which account for large portion of damages in security incidents. To achieve this, we employed a digital forensic indicator method that was previously utilized to collect information of client-side incidents.

• KIPS Transactions on Computer and Communication Systems
• /
• v.5 no.4
• /
• pp.95-102
• /
• 2016

Computer security incident such as confidential information leak and data destruction are constantly growing and it becomes threat to information in digital devices. To respond against the incident, digital forensic techniques are also developing to help digital incident investigation. With the development of digital forensic technology, a variety of forensic artifact has been developed to trace the behavior of users. Also, a diversity of forensic tool has been developed to extract information from forensic artifact. However, there is a issue that information from forensic tools has its own forms. To solve this problem, it needs to process data when it is output from forensic tools. Then it needs to compare and analyze processed data to identify how data is related each other and interpret the implications. To reach this, it calls for effective method to store and output data in the course of data processing. This paper aims to propose DFIOC (Digital Forensic Indicators Of Compromise) that is capable of transcribing a variety of forensic artifact information effectively during incident analysis and response. DFIOC, which is XML based format, provides "Evidence" to represent various forensic artifacts in the incident investigation. Furthermore, It provides "Forensic Analysis" to report forensic analysis result and also gives "Indicator" to investigate the trace of incidence quickly. By logging data into one sheet in DFIOC format for forensic analysis process, it is capable of avoiding unnecessary data processing. Lastly, since collected information is recorded in a normalized format, data input and output becomes much easier as well as it will be convenient to use for identification of collected information and analysis of data relationship.