• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.287-294
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• 2016

APT attack aimed at the interruption of information and communication facilities and important information leakage of companies. it performs an attack using zero-day vulnerabilities, social engineering base on collected information, such as IT infra, business environment, information of employee, for a long period of time. Fragmentary response to cyber threats such as malware signature detection methods can not respond to sophisticated cyber-attacks, such as APT attacks. In this paper, we propose a cyber intrusion detection model for countermeasure of APT attack by utilizing heterogeneous system log into big-data. And it also utilizes that merging pattern-based detection methods and abnormality detection method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.6
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• pp.1263-1269
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• 2014

Since the cyber defense has been dependent on commercial products and protection systems, in aspect of the recent trends, our cyber defence ecosystem can be more vulnerable. In case of general defense weapon companies, they have to be observed by the government such as certain proprietary technologies and products for the protection from the enemy. On the contrary, most cyber weapon companies have not been managed like that. For this reason, cyber attack can reach to the inside of our military through the security hole of commercial products. In this paper, we enhanced a military cyber protection ecosystems out of enemy attacks and analyze the hypothetical scenarios to evaluate and verify the vulnerability, and finally more securable ecosystem of military protection system is presented politically and technically.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.158-161
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• 2018

2011년 미국에서 최초로 소개된 후 기존 보안 기술과 다른 새로운 정보시스템 보호 기술로 Moving Target Defense(MTD)가 활발히 연구 되고 있다. MTD는 시스템의 구성 요소들을 뷸규칙적이고 동적으로 변화시켜 공격표면(Attack surface)을 줄임으로써 외부 공격에 대한 보안성을 높인다. 주로 시스템 정보를 수집 및 분석하여 공격하는 보안 위협들에 효과적이며 특히 지능형 지속 보안 위협(Advanced Persistent Threat), 킬 체인(Kill-Chain) 보안에 뛰어난 성능을 기대할 수 있다. 최근 MTD 시스템 구현 및 개발로 상용화가 시작되었으나 MTD 활용을 통해 어느 정도의 보안성 및 효율성을 가지는지에 대한 성능 평가인증, 시험지침 등이 표준화 되어있지 않아 기준이 모호한 실정이다. 본 논문에서는 이러한 최근 MTD 이슈에 대해 살펴보고 MTD와 연관 되어있는 각 분야에 어떤 평가인증 요구사항들이 있는지 분석한다. 이를 통해 MTD에 어떠한 평가인증 요구사항이 있는지 도출하여 앞으로 MTD 평가인증 표준화 참고 및 활용에 기여 할 수 있을 것으로 전망한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2805-2826
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• 2021

In most cases, the routing policy of networks shows a preference for a static one-to-one mapping of communication pairs to routing paths, which offers adversaries a great advantage to conduct thorough reconnaissance and organize an effective attack in a stress-free manner. With the evolution of network intelligence, some flexible and adaptive routing policies have already proposed to intensify the network defender to turn the situation. Routing mutation is an effective strategy that can invalidate the unvarying nature of routing information that attackers have collected from exploiting the static configuration of the network. However, three constraints execute press on routing mutation deployment in practical: insufficient route mutation space, expensive control costs, and incompatibility. To enhance the availability of route mutation, we propose an OpenFlow-based route mutation technique called Polymorphic Path Transferring (PPT), which adopts a physical and virtual path segment mixed construction technique to enlarge the routing path space for elevating the security of communication. Based on the Markov Decision Process, with considering flows distribution in the network, the PPT adopts an evolution routing path scheduling algorithm with a segment path update strategy, which relieves the press on the overhead of control and incompatibility. Our analysis demonstrates that PPT can secure data delivery in the worst network environment while countering sophisticated attacks in an evasion-free manner (e.g., advanced persistent threat). Case study and experiment results show its effectiveness in proactively defending against targeted attacks and its advantage compared with previous route mutation methods.

• 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.23 no.4
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• pp.33-41
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• 2023

In recent years, advanced persistent threat (APT) attack organizations have exploited various vulnerabilities and attack techniques to target companies and institutions with national core technologies, distributing ransomware and demanding payment, stealing nationally important industrial secrets and distributing them on the black market (dark web), selling them to third countries, or using them to close the technology gap, requiring national-level security preparations. In this paper, we analyze the attack methods of attack organizations such as Kimsuky and Lazarus that caused industrial secrets leakage damage through APT attacks in Korea using the MITRE ATT&CK framework, and derive 26 cybersecurity-related administrative, physical, and technical security requirements that a company's security system should be equipped with. We also proposed a security framework and system configuration plan to utilize the security requirements in actual field. The security requirements presented in this paper provide practical methods and frameworks for security system developers and operators to utilize in security work to prevent leakage of corporate industrial secrets. In the future, it is necessary to analyze the advanced and intelligent attacks of various APT attack groups based on this paper and further research on related security measures.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.1-8
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• 2020

Recently, the number of APT(Advanced Persistent Threats) attack using malware has been increasing, and research is underway to prevent and detect them. While it is important to detect and block attacks before they occur, it is also important to make an effective response through an accurate analysis for attack case and attack type, these respond which can be determined by analyzing the attack group of such attacks. Therefore, this paper propose a framework based on genetic algorithm for analyzing malware and understanding attacker group's features. The framework uses decompiler and disassembler to extract related code in collected malware, and analyzes information related to author through code analysis. Malware has unique characteristics that only it has, which can be said to be features that can identify the author or attacker groups of that malware. So, we select specific features only having attack group among the various features extracted from binary and source code through the authorship clustering method, and apply genetic algorithm to accurate clustering to infer specific features. Also, we find features which based on characteristics each group of malware authors has that can express each group, and create profiles to verify that the group of authors is correctly clustered. In this paper, we do experiment about author classification using genetic algorithm and finding specific features to express author characteristic. In experiment result, we identified an author classification accuracy of 86% and selected features to be used for authorship analysis among the information extracted through genetic algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1247-1258
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• 2018

The Cyber Kill Chain originally proposed by Lockheed Martin defines the standard procedure of general cyber attacks and suggests tailored defensive actions per each step, eventually neutralizing the intent of the attackers. Defenders can effectively deal with Advanced Persistent Threat(APT)s which are difficult to be handled by other defensive mechanisms under the Cyber Kill Chain. Recently, however, social engineering techniques that exploits the vulnerabilities of humans who manage the target systems are prevail rather than the technical attacks directly attacking the target systems themselves. Under the circumstance, the Cyber Kill Chain model should evolve to encompass social engineering attacks for the improved effectiveness. Therefore, this paper aims to establish a definite concept of Cyber Kill Chain for social engineering based cyber attacks, called Social Engineering Cyber Kill Chain, helping future researchers in this literature.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.267-279
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• 2017

This study emulated unscheduled phishing e-mails over a long period of time by imitating the manner in which external hackers attacked a group of employees in a company. We then measured and analyzed the recipient's ability to identify and respond to phishing e-mails as training progressed. In addition, we analyzed the changes in participants' response behavior when changing the external control condition between the training. As a result of the analysis, it was confirmed that the training duration had a positive (+) relationship with the employees' ability to identify phishing e-mails and the infection rate, and more employees read emails and infected with phishing attacks using social issues and seasonal events. It was also confirmed that reinforcement of internal control policy on infected persons affects positively (+) on the phishing attack response behavior of employees. Based on these results, we would like to suggest the right training method for each organization to enhance the ability of employees to cope with phishing attacks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.317-320
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and Classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on December in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attack thus far. Therefore, we will use big data analytics to analyze whether or not APT attack has occurred in order to defend against the manipulative attackers. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean defense system. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attack and evaluate the models' accuracy and other results. Lastly, we will present an effective response method to address a detected APT attack.