• Convergence Security Journal
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• v.20 no.5
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• pp.27-32
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• 2020

The Windows Event Log is a Log that defines the overall behavior of the system, and these files contain data that can detect various user behaviors and signs of anomalies. However, since the Event Log is generated for each action, it takes a considerable amount of time to analyze the log. Therefore, in this study, we designed and implemented an XML-based Event Log analysis tool based on the main Event Log list of "Spotting the Adversary with Windows Event Log Monitoring" presented at the NSA.

• Convergence Security Journal
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• v.18 no.3
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• pp.77-85
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• 2018

With the convergence of communications network between control system and public network, such threats like information leakage/falsification could be fully shown in control system through diverse routes. Due to the recent diversification of security issues and violation cases of new attack techniques, the security system based on the information database that simply blocks and identifies, is not good enough to cope with the new types of threat. The current control system operates its security system focusing on the outside threats to the inside, and it is insufficient to detect the security threats by insiders with　the authority of security access. Thus, this study conducted the importance analysis based on the main event log list of "Spotting the Adversary with Windows Event Log Monitoring" announced by NSA. In the results, the matter of importance of event log for the detection of insider threats to control system was understood, and the results of this study could be contributing to researches in this area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.591-603
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• 2018

Windows Event Log is a format that records system log in Windows operating system and methodically manages information about system operation. An event can be caused by system itself or by user's specific actions, and some event logs can be used for corporate security audits, malware detection and so on. In this paper, we choose actions related to corporate security audit and malware detection (External storage connection, Application install, Shared folder usage, Printer usage, Remote connection/disconnection, File/Registry manipulation, Process creation, DNS query, Windows service, PC startup/shutdown, Log on/off, Power saving mode, Network connection/disconnection, Event log deletion and System time change), which can be detected through event log analysis and classify event IDs that occur in each situation. Also, the existing event log tools only include functions related to the EVTX file parse and it is difficult to track user's behavior when used in a forensic investigation. So we implemented new analysis tool in this study which parses EVTX files and user behaviors.

• Convergence Security Journal
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• v.17 no.5
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• pp.35-40
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• 2017

The control system can have such threats as information leakage and falsification through various routes due to communications network fusion with public network. As the issues about security and the infringe cases by new attack methods are diversified recently, with the security system that makes information data database by simply blocking and checking it is difficult to cope with new types of threats. It is also difficult to respond security threats by insiders who have security access authority with the existing security equipment. To respond the threats by insiders, it is necessary to collect and analyze Event Log occurring in the internal system realtime. Therefore, this study could find out whether there is correlation of the elements among Event Logs through correlation analysis based on Event Logs that occur real time in the control system, and based on the analysis result, the study is expected to contribute to studies in this field.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.101-112
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• 2024

In this paper, we present EDF (Event Data Factory), an interactive tool designed to assist event log generation for process mining. EDF integrates various data connectors to improve its capability to assist users in connecting to diverse data sources. Our tool employs low-code/no-code technology, along with graph-based visualization, to help non-expert users understand process flow and enhance the user experience. By utilizing metadata information, EDF allows users to efficiently generate an event log containing case, activity, and timestamp attributes. Through log quality metrics, our tool enables users to assess the generated event log quality. We implement EDF under a cloud-based architecture and run a performance evaluation. Our case study and results demonstrate the usability and applicability of EDF. Finally, an observational study confirms that EDF is easy to use and beneficial, expanding small and medium-sized enterprises' (SMEs) access to process mining applications.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.544-547
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• 2004

Current increasingly information systems log historic information in a systematic way. Not only workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called 'event log'. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this problem by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach and presents a tool to mine social networks from event logs.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.16-21
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• 2020

Owing to the convergence of the communication network with the control system and public network, security threats, such as information leakage and falsification, have become possible through various routes. If we examine closely at the security type of the current control system, the operation of the security system focuses on the threats made from outside to inside, so the study on the detection system of the security threats conducted by insiders is inadequate. Thus, this study, based on "Spotting the Adversary with Windows Event Log Monitoring," published by the National Security Agency, found that event logs can be utilized for the detection and maneuver of threats conducted by insiders, by analyzing the validity of detecting insider threats to the control system with the list of important event logs.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.49-55
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• 2019

Organizations design and operate business process models to achieve their goals efficiently and systematically. With the advancement of IT technology, the number of items that computer systems can participate in and the process becomes huge and complicated. This phenomenon created a more complex and subdivide flow of business process.The process instances that contain workcase and events are larger and have more data. This is an essential resource for process mining and is used directly in model discovery, analysis, and improvement of processes. This event log is getting bigger and broader, which leads to problems such as capacity management and I / O load in management of existing row level program or management through a relational database. In this paper, as the event log becomes big data, we have found the problem of management limit based on the existing original file or relational database. Design and apply schemes to archive and analyze large event logs through Hadoop, an open source distributed file system, and HBase, a NoSQL database system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.387-396
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• 2016

As the number of people using digital devices has increased, the digital forensic, which aims at finding clues for crimes in digital data, has been developed and become more important especially in court. Together with the development of the digital forensic, the anti-forensic which aims at thwarting the digital forensic has also been developed. As an example, with anti-forensic technology the criminal would delete an digital evidence without which the investigator would be hard to find any clue for crimes. In such a case, recovery techniques on deleted or damaged information will be very important in the field of digital forensic. Until now, even though EVTX(event log)-based recovery techniques on deleted files have been presented, but there has been no study to retrieve event log data itself, In this paper, we propose some recovery algorithms on deleted or damaged event log file and show that our recovery algorithms have high success rate through experiments.