• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.807-816
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• 2018

Threat hunting is defined as a process of proactively and iteratively searching through networks to detect and isolate advanced threats that evade existing security solutions. The main concept of threat hunting is to find out weak points and remedy them before actual cyber threat has occurred. And HMM(Hunting Maturity Matrix) is suggested to evolve hunting processes with five levels, therefore, CSOC(Cyber Security Operations Center) can refer HMM how to make them safer from complicated and organized cyber attacks. We are developing a system for cyber situation awareness system with pro-active threat hunting process called unMazeTM. With this unMaze, it can be upgraded CSOC's HMM level from initial level to basic level. CSOC with unMaze do threat hunting process not only detecting existing cyber equipment post-actively, but also proactively detecting cyber threat by fusing and analyzing cyber asset data and threat intelligence.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.793-799
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• 2021

Recently, there has been a growing interest in threat hunting presented to overcome the limitations of existing security solutions. Threat hunting is generally recognized as a technique for identifying and eliminating threats that exit inside the system. But, the definition is not clear, so there is confusion in terms with penetration testing, intrusion detection, and incident analysis. Therefore, in this paper, compare and analyze the definitions of threat hunting extracted from reports and papers to clarify their implications and compare with defense techniques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.15-23
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• 2021

In most hacking attacks, hackers intrudes inside for a long period of time and attempts to communicate with the outside using a circumvent connection to achieve purpose. research in response to advanced and intelligent cyber threats has been mainly conducted with signature-based detection and blocking methods, but recently it has been extended to threat hunting methods. attacks from organized hacking groups are advanced persistent attacks over a long period of time, and bypass remote attacks account for the majority. however, even in the intrusion detection system using intelligent recognition technology, it only shows detection performance of the existing intrusion status. therefore, countermeasures against targeted bypass rwjqthrwkemote attacks still have limitations with existing detection methods and threat hunting methods. in this paper, to overcome theses limitations, we propose a model that can detect the targeted circumvent connection remote attack threat of an organized hacking group. this model designed a threat hunting process model that applied the method of verifying the origin IP of the remote circumvent connection, and verified the effectiveness by implementing the proposed method in actual defense information system environment.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.177-189
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• 2023

Malware detection is an increasingly important operational focus in cyber security, particularly given the fast pace of such threats (e.g., new malware variants introduced every day). There has been great interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis. In this paper, we present a deep recurrent neural network solution as a stacked Long Short-Term Memory (LSTM) with a pre-training as a regularization method to avoid random network initialization. In our proposal, we use global and short dependencies of the inputs. With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting. The proposed method speeds up the convergence (in comparison to stacked LSTM) by reducing the length of malware OpCode or bytecode sequences. Hence, the complexity of our final method is reduced. This leads to better accuracy, higher Mattews Correlation Coefficients (MCC), and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time. Our proposed method can be applied in real-time malware threat hunting, particularly for safety critical systems such as eHealth or Internet of Military of Things where poor convergence of the model could lead to catastrophic consequences. We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis. For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method. More specifically, of our proposed method achieves an accuracy of 99.1% in detecting IoT malware samples, with AUC of 0.985, and MCC of 0.95; thus, outperforming standard LSTM based methods in these key metrics.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.721-736
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• 2023

The Command and Control Framework was developed for penetration testing and education purposes, but threat actors such as cybercrime groups are abusing it. From a cyber threat hunting perspective, identifying Command and Control Framework servers as well as proactive responding such as blocking the server can contribute to risk management. Therefore, this paper proposes a methodology for tracking the Command and Control Framework in advance. The methodology consists of four steps: collecting a list of Command and Control Framework-related server, emulating staged delivery, extracting botnet configurations, and collecting certificates that feature is going to be extracted. Additionally, experiments are conducted by applying the proposed methodology to Cobalt Strike, a commercial Command and Control Framework. Collected beacons and certificate from the experiments are shared to establish a cyber threat response basis that could be caused from the Command and Control Framework.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.865-889
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• 2019

The need for cyber resilience is increasingly important in our technology-dependent society where computing devices and data have been, and will continue to be, the target of cyber-attackers, particularly advanced persistent threat (APT) and nation-state/sponsored actors. APT and nation-state/sponsored actors tend to be more sophisticated, having access to significantly more resources and time to facilitate their attacks, which in most cases are not financially driven (unlike typical cyber-criminals). For example, such threat actors often utilize a broad range of attack vectors, cyber and/or physical, and constantly evolve their attack tactics. Thus, having up-to-date and detailed information of APT's tactics, techniques, and procedures (TTPs) facilitates the design of effective defense strategies as the focus of this paper. Specifically, we posit the importance of taxonomies in categorizing cyber-attacks. Note, however, that existing information about APT attack campaigns is fragmented across practitioner, government (including intelligence/classified), and academic publications, and existing taxonomies generally have a narrow scope (e.g., to a limited number of APT campaigns). Therefore, in this paper, we leverage the Cyber Kill Chain (CKC) model to "decompose" any complex attack and identify the relevant characteristics of such attacks. We then comprehensively analyze more than 40 APT campaigns disclosed before 2018 to build our taxonomy. Such taxonomy can facilitate incident response and cyber threat hunting by aiding in understanding of the potential attacks to organizations as well as which attacks may surface. In addition, the taxonomy can allow national security and intelligence agencies and businesses to share their analysis of ongoing, sensitive APT campaigns without the need to disclose detailed information about the campaigns. It can also notify future security policies and mitigation strategy formulation.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.1
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• pp.1-6
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• 2022

Among the 13 species of otters in the world, only one Eurasian otter (Lutra lutra) is found in South Korea. In the Korean Peninsula, otter pelts were historically valuable and expensive commodities used for international trade, and otters have long been poached as hunting animals. Recent rapid economic development in South Korea has increased habitat fragmentation and loss, creating a continuing threat to the natural environment. Otters live only in the area of rivers and streams as a family group and are territorial (linear habitat). Due to these limited conditions of otter habitat, the population size of otter is lower than that of onshore mammals. According to recent research, DNA analyses using microsatellite markers have shown that only approximately 7-21 otter individuals inhabit river systems for a length of 50-230 km. Korea's urban streams are associated with many threats that hinder otters from inhabiting them. Many areas around the urban streams are surrounded by high concrete riverbanks, and the risk of roadkill is also high. Nevertheless, ecological restoration projects in the urban rivers will contribute greatly to the stable inhabitation of otters. Detailed otter conservation strategies, such as the elimination of threat factors, improvement of habitat environment, and restoration of food resources and shelter, will provide a positive restoration effect on otter and river ecosystems as well.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.5
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• pp.1191-1204
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• 2012

As attackers try to paralyze information security systems, many researchers have investigated security testing to analyze vulnerabilities of information security products. Penetration testing, a critical step in the development of any secure product, is the practice of testing a computer systems to find vulnerabilities that an attacker could exploit. Security testing like penetration testing includes gathering information about the target before the test, identifying possible entry points, attempting to break in and reporting back the findings. Therefore, to obtain maximum generality, re-usability and efficiency is very useful for efficient security testing and vulnerability hunting activities. In this paper, we propose a threat analysis based software security testing technique for evaluating that the security functionality of target products provides the properties of self-protection and non-bypassability in order to respond to attacks to incapacitate or bypass the security features of the target products. We conduct a security threat analysis to identify vulnerabilities and establish a testing strategy according to software modules and security features/functions of the target products after threat analysis to improve re-usability and efficiency of software security testing. The proposed technique consists of threat analysis and classification, selection of right strategy for security testing, and security testing. We demonstrate our technique can systematically evaluate the strength of security systems by analyzing case studies and performing security tests.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.203-208
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• 2017

Recently, the youth unemployment becomes the significant social problems and various industry-university cooperation projects those reflect the job site oriented education are planned and fulfilled at the university in order to help graduates to get the job easily. These efforts, however, becomes job hunting failure or suffers lots of trials and errors without reflecting the correct circumstances of the job market properly. In this paper, we propose the business model for training the 4th grade students as a job site oriented software engineers in BLE fields through tight industry-university cooperation and fulfill the related project. After then, we search for the problems and failure factors of the business model through the correspondence strategy analysis for weaknesses and threat factors and propose the improved and successful business model.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.618-628
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• 2020

The purpose of this paper is to propose a new security orchestration service model by combining various security solutions that have been introduced and operated individually as a basis for cyber security framework. At present, in order to respond to various and intelligent cyber attacks, various single security devices and SIEM and AI solutions that integrate and manage them have been built. In addition, a cyber security framework and a security control center were opened for systematic prevention and response. However, due to the document-oriented cybersecurity framework and limited security personnel, the reality is that it is difficult to escape from the control form of fragmentary infringement response of important detection events of TMS / IPS. To improve these problems, based on the model of this paper, select the targets to be protected through work characteristics and vulnerable asset identification, and then collect logs with SIEM. Based on asset information, we established proactive methods and three detection strategies through threat information. AI and SIEM are used to quickly determine whether an attack has occurred, and an automatic blocking function is linked to the firewall and IPS. In addition, through the automatic learning of TMS / IPS detection events through machine learning supervised learning, we improved the efficiency of control work and established a threat hunting work system centered on big data analysis through machine learning unsupervised learning results.