• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.69-79
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• 2013

Recently, many enterprises are suffering from advanced types of malware and their variants including intelligent malware that can evade the current security systems. This addresses the fact that current security systems have limits on protecting advanced and intelligent security threats. To enhance the overall level of security, first of all, it needs to increase detection ratio of each security solution within a security system. In addition, it is also necessary to implement internetworking between multiple security solutions to increase detection ratio and response speed. In this paper, we suggest a collaborative security response method to overcome the limitations of the previous Internet service security solutions. The proposed method can show an enhanced result to respond to intelligent security threats.

• Convergence Security Journal
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• v.21 no.2
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• pp.11-18
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• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.1-12
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• 2004

As network systems are developed rapidly and network architectures are more complex than before, it needs to use PBNM(Policy-Based Network Management) in network system. Generally, architecture of the PBNM consists of two hierarchical layers: management layer and enforcement layer. A security policy server in the management layer should be able to generate new policy, delete, update the existing policy and decide the policy when security policy is requested. And the security policy server should be able to analyze and manage the alert messages received from Policy enforcement system in the enforcement layer for the available information. In this paper, we propose an alert analyzer using data mining. First, in the framework of the policy-based network security management, we design and implement an alert analyzes that analyzes alert data stored in DBMS. The alert analyzer is a helpful system to manage the fault users or hosts. Second, we implement a data mining system for analyzing alert data. The implemented mining system can support alert analyzer and the high level analyzer efficiently for the security policy management. Finally, the proposed system is evaluated with performance parameter, and is able to find out new alert sequences and similar alert patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.107-115
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• 2005

ATC (Abnormal traffic controller) is presented as next generation security technology to securely support reliable Internet service and to guarantee network survivability, which is deployed in Internet access point. The key concept of the ATC is abnormal traffic monitoring and traffic control technology. When fault factors exist continuously and/or are repeated, abnormal traffic control guarantees service completeness as much as possible. The ATC with control policy on abnormal traffic is superior to the ATC with blocking policy as well as conventional network node, when the ratio of effective traffic to abnormal traffic is higher than $30{\%}.$ When traffic intended unknown attack occurs, network IDS is high false positive probability and so is limited to apply. In this environment, the ATC can be a key player to help the network node such as router to control abnormal traffic.

• Korean Security Journal
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• no.60
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• pp.113-135
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• 2019

Despite being one of the most important national facilities, the National Assembly building of the Republic of Korea has become increasingly vulnerable to potential terrorist attacks, and the instances of demonstrations occurring around and banned items taken into the building are continuing to rise. In addition, promoting the idea of "open assembly" has led to increased visitors and weakened access control. Furthermore, while there is a significant symbolic value attached to attacking the National Assembly building, the level of security management is relatively very low, making it a suitable target for terrorism. In order to address such vulnerability, an appropriate access control system should be in place from the areas surrounding the building. However, the National Assembly Security Service which oversees security around the building is scheduled to disband in June 2020 following the abolition of the conscripted police force in 2023. Therefore, there needs to be an alternative option to bolster the security system outside the facility. In this research, the perceptions of 114 government officials in charge of security at the National Assembly Secretariat toward the protection and security system of the areas surrounding the National Assembly building were examined. Results showed that the respondents believed it was highly likely that risky situations could occur outside the building, and the use of advanced technologies such as intelligent video surveillance, intrusion detection system, and drones was viewed favorably. Moreover, a mid- to long-term plan of establishing a unified three-layer protection system and designating a department in charge of the security outside the building were perceived positively. Lastly, the participants supported the idea of employing private police to replace the National Assembly Security Service for the short term and introducing parliamentary police for the mid- to long-term.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.61-68
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• 2019

Although the number of smartphone users is continuously increasing due to the advantage of being able to easily use most of the Internet services, the number of counterfeit applications is rapidly increasing and personal information stored in the smartphone is leaked to the outside. Because Android app was developed with Java language, it is relatively easy to create counterfeit apps if attacker performs the de-compilation process to reverse app by abusing the repackaging vulnerability. Although an obfuscation technique can be applied to prevent this, but most mobile apps are not adopted. Therefore, it is fundamentally impossible to block repackaging attacks on Android mobile apps. In addition, personal information stored in the smartphone is leaked outside because it does not provide a forgery self-verification procedure on installing an app in smartphone. In order to solve this problem, blockchain is used to implement a process of certificated application registration and a fake app identification and detection mechanism is proposed on Hyperledger Fabric framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.955-961
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• 2021

In modern warfare, the importance of electronic warfare, which carries out a mission that using radio wave to find out enemy information or to protect ally information, has increased. Radar jamming technique is one of the most representative techniques of EA(Electronic Attack), it disturbs and deceives enemy radar system in order to secure ally location information. Velocity deception jamming technique, which is one of the radar jamming techniques, generally operate against pulse-doppler radar which use doppler effect in order to track target's velocity and location. Velocity Deception Jamming Technique can be implemented using DRFM(Digital Radio Frequency Memory) that performs Frequency Modulation. In this paper, I describe implementation method of VGPO/VGPI(Velocity Gate Pull-Off/Pull-In) velocity deception jamming technique using phase-sampled DRFM, and verify the operation of VGPO/VGPI velocity deception jamming technique with board test under signal injection condition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1019-1034
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• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.