• Convergence Security Journal
• /
• v.20 no.5
• /
• pp.27-32
• /
• 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
• /
• v.18 no.3
• /
• pp.77-85
• /
• 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
• /
• v.28 no.3
• /
• pp.591-603
• /
• 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
• /
• v.17 no.5
• /
• pp.35-40
• /
• 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 information and communication convergence engineering
• /
• v.18 no.1
• /
• pp.16-21
• /
• 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 the Korea Institute of Information Security & Cryptology
• /
• v.26 no.2
• /
• pp.387-396
• /
• 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.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.1009-1017
• /
• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Journal of Korea Multimedia Society
• /
• v.23 no.1
• /
• pp.24-30
• /
• 2020

As the number of internet-connected appliances and the variety of IoT services are rapidly increasing, it is hard to protect IT assets with traditional network security techniques. Most traditional network log analysis systems use rule based mechanisms to reduce the raw logs. But using predefined rules can't detect new attack patterns. So, there is a need for a mechanism to reduce congested raw logs and detect new attack patterns. This paper suggests enterprise security management for IoT services using graph and network measures. We model an event network based on a graph of interconnected logs between network devices and IoT gateways. And we suggest a network clustering algorithm that estimates the attack probability of log clusters and detects new attack patterns.

• Journal of Digital Convergence
• /
• v.12 no.8
• /
• pp.351-359
• /
• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.772-786
• /
• 2021

The process of tracking suspicious behavior manually on a system and gathering evidence are labor-intensive, variable, and experience-dependent. The system logs are the most important sources for evidences in this process. However, in the Microsoft Windows operating system, the action events are irregular and the log structure is difficult to audit. In this paper, we propose a model that overcomes these problems and efficiently analyzes Microsoft Windows logs. The proposed model extracts lists of both common and key events from the Microsoft Windows logs to determine detailed actions. In addition, we show an approach based on the proposed model applied to track illegal file access. The proposed approach employs three-step tracking templates using Elastic Stack as well as key-event, common-event lists and identify event lists, which enables visualization of the data for analysis. Using the three-step model, analysts can adjust the depth of their analysis.