• Proceedings of the Korea Information Processing Society Conference
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• 2002.04b
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• pp.903-906
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• 2002

정보 인프라의 고도화와 인터넷 사용의 폭발적인 증가로 인해 다양한 형태의 정보를 대량으로 교환할 수 있는 환경이 마련되었으며, 정보기술의 보편화를 통해 누구든지 쉽게 기술을 습득하고 이동하게 되었다. 인터넷 사용자는 크게 일반 사용자 및 적대적 사용자로 분류될 수 있으며, 특히 적대적 사용자는 정보의 불법적인 유출, 악용, 파괴할 수 있는 고도의 기술을 지닌 그룹으로서 인터넷의 존재 자체를 위협할 수 있는 수준이며, 이들의 기술은 날로 지능화되고 자동화되는 추세이다. 정보의 가치가 중요해 지면서 고급 정보에 대한 피해 사례가 늘어가고 있으나, 이를 정확하게 발견하고 신속하게 대처하기 위한 기술의 개발은 아직 초보 단계에 머무르고 있다. 대부분의 보안 시스템이 침입에 대한 탐지 및 대응 기술 개발에 역점을 두고 있으나, 알려지지 않은 침입에 대해서는 정확한 탐지 및 신속한 대응이 어렵다. 본 논문에서는 가상 서비스를 통해 침입자를 유도하고, 침입 과정 및 기법을 학습함으로써 새로운 기법에 대한 신속한 대응책을 수립할 수 있도록 해 주는 HoneyPot의 구현에 관한 방법을 제시한다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.479-481
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• 2014

개인정보보호법의 개정안이 1년간의 계도 기간을 거쳐 2014년 8월 7일에 시행되었다. 개인정보 보호법은 개인정보보호에 관한 법률이 포함하고 있으며 크게 관리적인 보호조치와 기술적 보호 조치로 구분된다. 본 시스템은 기술적인 보호조치에 해당하며, 개인정보가 포함된 문서를 탐지 및 암 복호화 한다. 개인 정보를 보호하는 방법에 있어서 개인정보에 해당하는 패턴을 정의하고, 상기 패턴을 참조하여 컴퓨터에 저장된 파일에 개인정보가 포함되어 있는가를 검색하며, 정보보호를 위한 암 복호화 동작을 수행하는 정보보호단계를 제공함으로써, 개인정보가 포함되어 있는 파일의 외부 유출을 차단할 수 있을 것으로 기대된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2B
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• pp.138-147
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• 2012

With the improvement of wireless communication and embedded technology, WSN is used at various fields. Meanwhile, because WSN is resource constrained, it is more vulnerable than other networks. To solve the security problem of WSN, we can use the traditional secure mechanism like as cryptography and authentication. But the traditional secure mechanism is not enough for all security issues that may be happened in WSN, especially attacks caused by the compromised node. So, we need the IDS as the second secure mechanism for WSN. In this paper, we propose the Specification-based Intrusion Detection Mechanism that makes LEACH, which is one of the clustering routing protocol for WSN, more reliable and safety.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.616-620
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• 2006

한 해에도 수많은 해킹 사고가 발생하고 있고, 이 중에서 웹 해킹이 차지하는 비율은 급격하게 증가하고 있다. 또한 최근의 해킹 동향을 분석해 보았을 때 웹 해킹의 비율은 더욱 증가할 것이라고 예상된다. HTTP 프로토콜을 이용한 공격의 특성 상 정상행위와 비정상 행위의 구분이 어렵다. 따라서 웹 서비스에 특화된 침입탐지 시스템이 요구된다. 또한 웹 사이트 관리자는 빠른 탐지와 대응을 위해 이상 행위에 대한 신속하고 정확한 인식을 필요로 한다. 본 논문에서는 이러한 필요성을 기반으로 Location-based Visualization Tool을 제안한다. 웹 사용 현황 및 이상행위에 대해 시각적인 정보를 제공하기 위해 웹 서버의 access log를 분석하여 이상 행위를 탐지하였고, IP정보를 기반으로 지역 정보의 시각화를 구현하였다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.5
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• pp.786-795
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• 1999

To detect the precise of arrival of target signal in real ocean environments, Inverse beamformnig(IBF) solutions to the Inverse beamforming integral equation are surveyed theoretically and the performance properties of the IBF are analyzed with simulations. IBF-Cardioid beamforming algorithm is proposed for port/starboard discrimination and the performance gains are studied with simulations. It is shown that IBF has a 3 dB array noise gain advantage over CBF under ideal conditions. This 3 dB array noise gain advantage is proven by theocratical studies and simulations. This array noise gain advantage leads to a minimum detectable level advantage for IBF output compared with CBF output. The fact that the IBF beamwidth is narrower than the CBF beamwidth by a factor of 0.68 proves the performance of detection and spatial resolution improvement. Comparing the simulation results of IBF-Cardioid beamforming and Conventional Cardioid beamforming, it is shown that IBF-Cardioid beamformer have performance enhancement in minimum detection level, detection accuracy and resolution.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.29-39
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• 2020

Online communities, such as Internet portal sites and social media, have become popular since they allow users to share opinions and to obtain information anytime, anywhere. Accordingly, an increasing number of opinions are manipulated to the advantage of particular groups or individuals, and these opinions include falsified product reviews and political propaganda. Existing detection systems are built upon the characteristics of manipulated opinions for one particular time period. However, manipulation tactics change over time to evade detection systems and to more efficiently spread information, so detection systems should also evolve according to the changes. We therefore propose a system that helps observe and trace changes in manipulation tactics. This system classifies opinions into clusters that represent different tactics, and changes in these clusters reveal evolving tactics. We evaluated the system with over a million opinions collected during three election campaigns and found various changes in (i) the times when manipulations frequently occur, (ii) the methods to manipulate recommendation counts, and (iii) the use of multiple user IDs. We suggest that the operators of online communities perform regular audits with the proposed system to identify evolutions and to adjust detection systems.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2003.10a
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• pp.35-38
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• 2003

현재, 세계 여러나라에서 해상물표를 정확하게 탐지 및 검출하기 위한 방안으로 레이더 clutter 신호를 효과적으로 억제 및 제거하기 위한 연구가 활발하게 진행되고 있다. 일반적으로 레이더 반사파에 대한 envelope 신호의 진폭는 Rayleigh 분포에 따라 변동하는 특성을 나타내지만, clutter의 진폭분포의 파라 메터가 변동하여 분포형상이 변화하면, 오경보확률(false alarm probability)에도 변화가 발생하기 때문에 오경보확률을 충분히 낮은 일정치로써 억제시켜 일정오경보확률(constant false alarm rate, CFAR)을 유지하는 처리가 필요하다. (중략)

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.637-639
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• 2003

인터넷이 널리 보급되면서 이용자들간에는 편리하고 빠른 정보교환이 가능하게 되었지만 이를 방해하는 해커들의 활동 또한 크게 증가하고 있다. 그 중 DDoS(Distributed Denial of Service) 공격은 인터넷 서비스를 하고 있는 서버에 심각한 해를 주며 탐지와 대응이 어려운 해킹방법중의 하나이다. 본 연구에서는 DDoS 공격 대응을 위해 액티브 네트워크를 이용해 개발한 DDoS 보안시스템[1][2]을 이용할 때 얼마나 효과적인 성능을 낼 수 있는지에 대한 분석 결과를 제공한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.