• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.947-950
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• 2014

The recent APT attacks including cyber terror are caused by a high level of malicious codes and hacking techniques. This implies that essentially, advanced security management is required, from the perspective of 5A. The changes of IT environment are represented by Mobile, Cloud and BYOD. In this situation, the security model needs to be changed, too into the Airport model which emphasizes prevention, and connection, security and integration of functions from the existing Castle model. This study suggested an application method of the risk-based Airport model to the cyber security environment.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.279-288
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• 2017

Nowadays, nations cyber security capabilities play an important role in a nation's defense. Security-critical infrastructures such as national defenses, public services, and financial services are now exposed to Advanced Persistent Threats (APT) and their resistance to such attacks effects the nations stability. Currently Cyber Threat Intelligence (CTI) is widely used by organizations to mitigate and deter APT for its ability to proactively protect their assets by using evidence-based knowledge. The evidence-based knowledge information can be exchanged among organizations and used by the receiving party to strengthen their cyber security management. This paper will discuss on the business process reengineering of the CTI information exchange management for a nationwide scaled control and governance by the government to better protect their national information security assets.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.179-187
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• 2023

APT cyber attacks have been a problem for over a past decade, but still remain a challenge today as attackers use more sophisticated techniques and the number of objects to be protected increases. 'Cyberattack Tracing System' allows analysts to find undetected attack codes that penetrated and hid in enterprises, and to investigate their lateral movement propagation activities. The enterprise is characterized by multiple networks and mass hosts (PCs/servers). This paper presents a data processing procedure that collects event data, generates a temporally and spatially extended provenance graph and cyberattack tracing paths. In each data process procedure phases, system design considerations are suggested. With reflecting the data processing procedure and the characteristics of enterprise environment, an operational architecture for CyberAttack Tracing System is presented. The operational architecture will be lead to the detailed design of the system.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.165-171
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• 2018

The most threatening attack that has become a hot topic of recent IT security is APT Attack.. So far, there is no way to respond to APT attacks except by using artificial intelligence techniques. Here, we have implemented a machine learning algorithm for analyzing cyber threat data using machine learning method, using a data set that collects cyber attack cases using Scikit Learn, a big data machine learning framework. The result showed an attack classification accuracy close to 70%. This result can be developed into the algorithm of the security control system in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.284-287
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• 2013

Recently target oriented attacks which target an enterprise and a government agency are increasing. The starting point of APT(Advanced Persistent Threat), called as target oriented attacks, is the spear phishing email that is personalized based on the information collected via Internet of the target personnel. It is known that 94% of target oriented attacks use spear phishing emails. Therefore, in this paper, we analysed spear phishing methods in detail and the characteristics and recent trends of spear phishing threats and proposed the effective prevention method of spear phishing for the end user.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.123-126
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• 2013

Access control system, when operating the infrastructure manager and the permissions for the user to clearly define the terminology that is. Various IT incidents still happening frequently occur, and these incidents in order to prevent the situation of access control is needed. In this study, the Copy command by hackers hacking incidents, such as walking dangerous limits for instructions attacks in advance, and also the internal administrator accident accidental limit command to walk off the risk in advance and even if the incident occurred access to the command history log and post it as evidence through the analysis techniques that can be utilized are described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4909-4926
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• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

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

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1045-1054
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• 2021

As APT attacks become more frequent and sophisticated, not only the advancement of the security systems but also the competence of the cybersecurity officers of each institution that operates them is becoming increasingly important. In a large-scale cyber defence exercise with many blue teams participating and many systems to simulate and defend against, it should be possible to simulate attacks to generate various attack patterns, network payloads, and system events. However, if one RT framework is used, there is a limitation that it can be easily detected by the blue team. In the case of operating multiple RT frameworks, a lot of time and effort by experts for exercise setup and operation for each framework is required. In this paper, we propose iRF(integrated RT framework) that can automatically operate large-scale cyber defence exercise by integrating a number of open RT frameworks and RT frameworks created by ourselves.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.13-22
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• 2021

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.