• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.451-456
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• 2005

This paper proposes an evaluation model to assessment of development and operation for maritime leisure in Mokpo Port.. The proposed model is combination of SWOT(Strength, Weakness, Opportunity, Threat) and AHP(Analytic Hierarchy Process) to evaluate development and operation for maritime leisure. The evaluation hierarchical structure is structured by ISM(Interpretive structural modeling) and composed of five level. At the third level, It combine SWOT into the assessment system. Strength and Weakness are internal factors. Opportunities and threats are external factors. There are economic and maritime leisure development in the model. There are three development and operation investment as Third-Sector, company, local organization. According to the results, the participants perceive prefer to strength and opportunity and found that the priority for the development and operation for maritime leisure of Third-Sector.

• Journal of Navigation and Port Research
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• v.29 no.8 s.104
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• pp.715-721
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• 2005

This paper proposes an evaluation model to assessment of development and operation for maritime leisure in Mokpo Port.. The proposed model is combination of SWOT(Strength, Weakness, Opportunity, Threat} and AHP(Analytic Hierarchy Process) to evaluate development and operation for maritime leisure. The evaluation hierarchical structure is structured by ISM(interpretive structural modeling} and composed of five level. At the third level, It combine SWOT into the assessment system Strength and Weakness are internal factors. Opportunities and threats are external factors. There are economic and maritime leisure development in the model. There are three development and operation investment as Third-Sector, company, local organization. According to the results, the participants perceive prefer to strength and opportunity and found that the priority for the development and operation for maritime leisure of Third-Sector.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.434-442
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• 2018

Recently, the threat to the security and damage of important data leaked by devices of intranet infected by malicious code through the Internet have been increasing. Therefore, the partitioned intranet model that blocks access to the server for business use by implementing authentication of devices connected to the intranet is required. For this, logical net partition with the VDI(Virtual Desktop Infrastructure) method is no information exchange between physical devices connected to the intranet and the virtual device so that it could prevent data leakage and improve security but it is vulnerable to the attack to expose internal data, which has access to the server for business connecting a nonregistered device into the intranet. In order to protect the server for business, we suggest a blockchain based network partition model applying blockchain technology to VDI. It contributes to decrease in threat to expose internal data by improving not only capability to verify forgery of devices, which is the vulnerability of the VDI based logical net partition, but also the integrity of the devices.

• Convergence Security Journal
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• v.22 no.2
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• pp.27-42
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• 2022

In the era of the Fourth Industrial Revolution, digital transformation to increase the effectiveness of industry is becoming more important to achieving the goal of industrial innovation. The digital new deal and smart defense are required for digital transformation and utilize artificial intelligence, big data analysis technology, and the Internet of Things. These changes can innovate the industrial fields of national defense, society, and health with new intelligent services by continuously expanding cyberspace. As a result, work productivity, efficiency, convenience, and industrial safety will be strengthened. However, the threat of cyber-attack will also continue to increase due to expansion of the new domain of digital transformation. This paper presents the risk scenarios of cyber-attack threats in the Fourth Industrial Revolution. Further, we propose a preemptive intrusion response model to bolster the complex security environment of the future, which is one of the fundamental alternatives to solving problems relating to cyber-attack. The proposed model can be used as prior research on cyber security strategy and technology development for preemptive response to cyber threats in the future society.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.69-86
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• 2003

A Protection Profile(PP) is a common security and assurance requirements for a specific class of Information Technology security products such as firewall and smart card. A PP should be included "TOE(Target of Evaluation) Security Environment", which is consisted of subsections: assumptions, treat, organizational security policies. This paper presents a new threats statement generation method for developing TOE security environment section of PP. Our survey guides the statement of threats in CC(Common Criteria) scheme through collected and analysed hundred of threat statements from certified and published real PPs and CC Tool Box/PKB that is included a class of pre-defined threat and attack statements. From the result of the survey, we present a new asset classification method and propose a threats statement generation model. The former is a new asset classification method, and the later is a production rule for a well formed statement of threats.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.55-63
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• 2016

SOA(service oriented architecture) based ESB(enterprise service bus) model is widely adopted in many companies for the safe processing of enormous data and the integration of business system. The existing web service technologies for the construction of SOA, however, show unsatisfactory in practical applications though the standardization of web service security technologies is in progress due to their limitations in safe exchange of data. Internal end users using a large business system based on such environment are composed of the variety of organizations and roles. Companies might receive more serious damage from insider threat than that from external one when internal end users get unauthorized information beyond the limits of their authority for private profit and bad purposes. In this paper, we propose a security architecture capable of identifying and coping with the security threats of web service technologies arouse from internal end users.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.67-74
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• 2011

In this paper, we presented a dynamic cyber attack tree which can describe an attack scenario flexibly for an active cyber attack model could be detected complex and transformed attack method. An attack tree provides a formal and methodical route of describing the security safeguard on varying attacks against network system. The existent attack tree can describe attack scenario as using vertex, edge and composition. But an attack tree has the limitations to express complex and new attack due to the restriction of attack tree's attributes. We solved the limitations of the existent attack tree as adding an threat occurrence probability and 2 components of composition in the attributes. Firstly, we improved the flexibility to describe complex and transformed attack method, and reduced the ambiguity of attack sequence, as reinforcing composition. And we can identify the risk level of attack at each attack phase from child node to parent node as adding an threat occurrence probability.

• 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.34 no.3
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• pp.417-430
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• 2024

As modern technology advances and the proliferation of the internet continues, cyber threats are also on the rise. To effectively counter these threats, the importance of utilizing Cyber Threat Intelligence (CTI) is becoming increasingly prominent. CTI provides information on new threats based on data from past cyber incidents, but the complexity of data and changing attack patterns present significant analytical challenges. To address these issues, this study aims to utilize graph data that can comprehensively represent multidimensional relationships. Specifically, the study constructs a heterogeneous graph based on malware data, and uses the metapath2vec node embedding technique to more effectively identify cyber attack groups. By analyzing the impact of incorporating topology information into traditional malware data, this research suggests new practical applications in the field of cyber security and contributes to overcoming the limitations of CTI analysis.