• Proceedings of the Korea Information Processing Society Conference
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• 2001.10b
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• pp.1049-1052
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• 2001

고가용 E-Business모델을 위해 구축된 나중 웹 클러스터 모델은 구조적 특성상 내부 시스템 노드들이 노출되어 있으며, 불법적인 3자에 의한 고의적인 방해와 공격으로 정상적인 작업수행이 불가능할 가능성을 지고 있다. 따라서 구성된 시스템 노드들을 보호하고 불법적인 사용자로부터의 정보유출과 부당한 서비스 요구를 효과적으로 대응할 수 있는 보안 시스템이 필요하나 제안한 분산 침입 탐지 시스템은 불법적인 침입을 탐지하기 위하여 일차적으로 Detection Agent를 이용한 작업요구 패킷의 검사를 수행하려, 이후 작업이 진행되었을 때 Monitoring Agent를 동하여 작업과정을 관찰하며 허용되지 않는 자원의 접근 및 요구가 발생하였을 때, 다른 시스템 노트와의 긴밀한 협조작업을 동해 침입여부를 판단한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.250-253
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• 2017

최근 급증하는 보안 침해 사고와 함께 보다 다양해지고 있는 해킹 기법들에 대한 대응책 마련이 논의되고 있다. 기업의 내부 인프라 자산 보호를 위해 조치를 취할 수 있는 기본적인 1차 대응책으로 네트워크 계층에서의 방어에 대한 중요성이 대두되고 있다. 본 논문에서는 일반 공중망(Public Network)과 분리된 가상의 사설망(VPN: Virtual Private Network)을 구현함으로써 기존의 VPN 모델보다 보안성이 뛰어나며 저렴한 비용으로 외부에서의 접근 및 스누핑(Snooping) 공격과 같은 보안위협에 대비할 수 있는 VPN 모델을 구축하여 실제 현업 망에서의 적용 가능성을 도출하고자 한다.

• Convergence Security Journal
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• v.21 no.1
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• pp.45-53
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• 2021

When the security monitoring system is performed in a separation network, there is little normal anomaly detection in internal networks or high-risk sections. Therefore, after the establishment of the security network, a model is needed to evaluate state-of-the-art cyber threat anomalies for internal network in separation network to complete the optimized security structure. In this study, We evaluate it by generating datasets of cyber vulnerabilities and malicious code arising from general and separation networks, It prepare for the latest cyber vulnerabilities in internal network cyber attacks to analyze threats, and established a cyber security test evaluation system that fits the characteristics. The study designed an evaluation model that can be applied to actual separation network institutions, and constructed a test data set for each situation and applied a real-time security assessment model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3045-3052
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• 2010

The international standard signature algorithm DSA has been guaranteed its security based on discrete logarithm problem. Recently, the DSA was known to be vulnerable to some fault analysis attacks in which the secret key stored inside of the device can be extracted by occurring some faults when the device performs signature algorithm. After analyzing an existing fault attack presented by Bao et al., this paper proposed a new fault analysis attack by disturbing the random number. Furthermore, we presented a countermeasure to compute DSA signature that has its immunity in the two types of fault attacks. The security and efficiency of the proposed countermeasure were verified by computer simulations.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.205-210
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• 2015

The auto industry has significantly evolved to the extent that much attention is paid to M2M (Machine-to-Machine) communication. In M2M communication which was first used in meteorology, environment, logistics, national defense, agriculture and stockbreeding, devices automatically communicate and operate in accordance with varying situations. M2M system is applied to vehicles, specifically to device-to-device communication inside cars, vehicle-to-vehicle communication, communication between vehicles and traffic facilities and that between vehicles and surroundings. However, communication systems are characterized by potential intruders' attacks in transmission sections, which may cause serious safety problems if vehicles' operating system, control system and engine control parts are attacked. Thus, device-to-device secure communication has been actively researched. With a view to secure communication between vehicular devices, the present study drew on hash functions and complex mathematical formulae to design a protocol, which was then tested with Casper/FDR, a tool for formal verification of protocols. In brief, the proposed protocol proved to operate safely against a range of attacks and be effective in practical application.

• Review of KIISC
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• v.18 no.5
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• pp.49-61
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• 2008

IT기술의 변화에 따라 금융기관의 정보보호 또한 안정성을 보장하면서도 새로운 비즈니스모델에 적합한 보안대책이 요구되고 있다. 금융 어플리케이션의 보안은 정보의 기밀성, 무결성, 가용성을 만족하는 안전하고 신뢰할 수 있는 시스템과 네트워크, 그리고 보안사고에 큰 비중을 차지하고 있는 내부 사용자에 대한 적절한 권한 부여와 접근통제가 요구되어진다. 정당한 사용자가 접근하여 발생하는 보안 문제, 즉 내부자에 의한 악의적인 행위나 오용, 실수 등에 의한 기업의 피해는 외부자에 의한 의도적인 공격보다 피해 규모가 크다. 따라서 정당한 사용자로 인증을 받았다고 할지라도 업무처리에 있어서 필요한 최소한의 권한만을 부여하는 것이 필요한 것이다. 이를 위해 금융기관에 적합한 접근통제가 필요하다. 역할기반 접근통제는 적용범위가 제한적인 강제적 접근통제와 분산된 보안관리로 중앙에서 통제가 어려운 자율적 접근통제의 단점을 보완하고, 실제 업무처리에 적합한 특성을 갖는다. 하지만 기존 역할기반 접근통제를 금융기관의 다양한 금융 어플리케이션에 적용하면 다음과 같은 문제가 발생할 수 있다. 첫째, 금융 어플리케이션에서 사용되는 역할 추출 및 관리가 어렵다. 둘째, 다양한 비즈니스모델이 원하는 직무분리가 복잡하고 어렵다. 셋째, 악의적인 내부 사용자가 역할을 변조하여 과도한 권한을 가질 수 있다. 따라서 본 논문에서는 기존의 역할기반 접근통제에 인사정보 연동을 통한 효율적인 역할 추출 및 분류방안과 역할관리, 직무분리의 세분화 그리고 역할의 안전한 관리를 위해 X.509기반의 권한관리 기반구조(PMI)를 이용한 권한관리 기술을 금융 어플리케이션 환경에 효율적으로 적용하는 방안을 제시한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.113-120
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• 2024

The population sparseness index(PSI) is being utilized to describe the functioning of internal layers in artificial neural networks from the perspective of neurons, shedding light on the black-box nature of the network's internal operations. There is research indicating a positive correlation between the PSI and performance in each layer of convolutional neural network models for image classification. In this study, we observed the internal operations of a convolutional neural network when adversarial examples were applied. The results of the experiments revealed a similar pattern of positive correlation for adversarial examples, which were modified to maintain 5% accuracy compared to applying benign data. Thus, while there may be differences in each adversarial attack, the observed PSI for adversarial examples demonstrated consistent positive correlations with benign data across layers.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.512-519
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• 2005

E-BLP security model considers the reliability of the processes that are real subjects in systems. This paper deals with the implementation of the E-BLP model for secure embedded systems. Implemented EBSM(E-BLP Based Security Module) consists of three components: identification and authentication, access control and BRC(Dynamic Reliability Check) that checks the process behavior dynamically. Access Control of EBSM ensures unreliable processes not to access the sensitive objects and the DRC detects the buffer overflow attack by normal user. Besides, the performance overhead of the embedded system applying the EBSM is introduced.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.679-691
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• 2016

As vehicle started to contain many different communication devices, collecting external information became possible in IoT environment. In such environment, remotely controling vehicle is possible when vehicle information is obtained by looking in to vehicle network through smart device. However, android based smart device applications are vulnerable to malicious modulation and redistribution. Modulated android application can lead to vehicle information disclosure that could bring about vehicle control accident which becomes threat to drivers. furthermore, since vehicles today does not contain security methods to protect it, they are very vulnerable to security threats which can cause serious damage to users and properties. In this paper, many different vehicle management android applications that are sold in Google Play has been analyzed. With this information, possible threats that could happen in vehicle management applications are being analysed to prove the risks. the experiment is done on actual vehicle to prove the risks. Also, access control method to protect the vehicle against malicious actions that could happen through external network in IoT environment is suggested in the paper.