• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.712-714
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• 2015

기존의 정보보호시스템들은 이미 확보된 시그니처 또는 이전에 분석된 정보를 기반으로 악성코드에 대응하고 있기 때문에, 시그니처가 알려지지 않은 악성코드 또는 변형된 악성코드의 경우, 탐지 및 식별에 한계를 지니고 있다. 본 연구는 이와 같은 문제를 해결하기 위해, 무결성을 검증하는 화이트리스트 기반의 응용프로그램 실행제어, 매체제어, 레지스트리 보호, 중요 파일 변경 방지, 프로세스 접근 역접속 IP/포트 통제 등의 기술을 복합적으로 적용하여, 악성코드의 침입뿐만 아니라 운영체제 및 응용프로그램 취약점을 기반으로 한 익스플로잇 공격으로부터 단말 PC를 더욱 확실하게 보호할 수 있도록 한 엔드포인트 응용프로그램 실행 통제 방안을 제시하였다. 본 연구의 결과는 프로토타입 형태로 개발하여 실 환경에서 통합테스트를 하여 공공기관, 금융기관, 통신사 등 실제 환경에 적합한 기술 및 기능임을 확인하였다. 본 연구를 통해, 실행 전 응용프로그램 무결성 검증과 실행 후 응용프로그램 실행 흐름 통제를 복합적으로 사용하여 알려진 악성코드 시그니처 정보에 의존한 기존 정보 보호 시스템과는 달리 알려지지 않은 악성코드까지 원천적으로 차단할 수 있을 것으로 기대된다.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.167-174
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• 2014

In this paper, we address a task allocation problem for a robot team that performs a search and attack mission. The robots are limited in sensing and communication capabilities, and carry different types of resources that are used to attack a target. The environment is uncertain and dynamic where no prior information about targets is given and dynamic events unpredictably happen. The goal of robot team is to collect total utilities as much as possible by destroying targets in a mission horizon. To solve the problem, we propose a distributed task allocation framework incorporated with effective resource management based on resource welfare. The framework we propose enables the robot team to retain more robots available by balancing resources among robots, and respond smoothly to dynamic events, which results in system performance improvement.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.379-381
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• 2004

지난 10년간 네트워크 기반의 컴퓨터 공격은 급격히 증가했으며 이에 따라 보안 기술도 발달하게 되었다. 침입탐지시스템은 컴퓨터 보안 기술로써 발전되어 왔으나 과도한 침입시도정보의 발생과 그 대부분이 긍정오류(false positive)를 발생시킴으로써 실제로 관리하는데 많은 어려움을 준다. 이러한 문제에 대안으로 여러 연구들이 진행되어 왔으며, 침입시도정보의 축약을 통한 관리적 측면에서의 효율을 높이는 연구도 진행되고 있다 그러한 연구들의 한 방법으로서 속성중심귀납법(Attribute Oriented induction, 이하 AOI)은 침입시도정보를 속성정보에 기반 하여 의미 있는 묶음으로 클러스터링 하는 방식이다 본 논문은 이 방식에서의 문제점을 분석하였으며 그 해결책으로써 본 논문에서는 위협수위 기반 AOI 클러스터링 기법을 제시하였다.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.210-213
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• 2010

센서 네트워크는 구성의 편리성과 이동성, 확장성이 뛰어난 장점을 가지고 있어서 침입 탐지나 원격감시 등 여러 분야에서 응용이 가능한 네트워크로 앞으로 계속 발전해야할 과제가 남아 있다. 즉, 센서 네트워크 환경에서는 보안성을 위해 센서 노드들은 키를 공유해야 하는데, 기존에 제안된 키 분배기법은 키가 너무 많이 필요하거나, 비 효율적인 문제가 있고 노드간 연결이 안되서 도태 되는 노드가 생기는 등 취약한 점이 많다. 본 논문에서는 무선 센서 네트워크 환경에서 안전한 키 분배를 위한 방법중에 쿼럼 시스템을 응용한 다중선택그리드 쿼럼방식을 사용해 센서 노드들 사이의 인증을 강화시켜 외부의 공격 노드들로부터 안전하고 노드간 연결성이 보장되는 방법을 제시하고 있다.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.563-577
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• 2017

Due to the Internet and computing capability, new and variant malware are discovered around 1 Million per day. Companies use dynamic analysis such as behavior analysis on virtual machines for unknown malware detection because attackers use unknown malware which is not detected by signature based AV effectively. But growing number of malware types are not only PE(Portable Executable) but also non-PE such as MS word or PDF therefore dynamic analysis must need more resources and computing powers to improve detection effectiveness. This study elicits the pre-filtering system evaluation factor to improve effective dynamic malware analysis system and presents and verifies the decision making model and the formula for solution selection using AHP(Analytics Hierarchy Process)

• The KIPS Transactions:PartC
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• v.10C no.1
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• pp.95-102
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• 2003

In order to guarantee the survivability of essential services against attacks based on new methodology, we need a solution to recognize important resources for the services and to adapt the urgent situation properly. In this paper, we present a dynamic resource reallocation scheme which is one of the core technologies for the construction of intrusion tolerant systems. By means of resource reallocation within a host, this scheme enables selected essential services to survive even after the occurrence if a system attack. Experimental result obtained on a test-bed reveals the validity of the proposed scheme for resource reallocation. This scheme may work together with IDS (Intrusion Detection System) to produce more effective responsive mechanisms against attacks.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.322-325
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• 2017

C와 C++는 컴파일된 결과물의 실행 속도가 타 프로그래밍 언어로 작성된 것보다 빠르고 기존의 시스템과 호환성이 좋다는 점 등의 장점을 갖고 있기 때문에 많은 프로그래머들로부터 여전히 사랑받는 프로그래밍 언어이다. 하지만 C와 C++로 생성된 프로그램은 버퍼 오버플로 취약점과 같은 다양한 메모리 커럽션 취약점을 갖고 있기 때문에 쉽게 외부의 공격자들에게 노출된다. 따라서 사용자가 이와 같은 언어로 생성된 프로그램을 안전하게 실행하기 위해서는 시스템에 신뢰할 수 있는 방어 기법이 미리 적용되어 있어야만 한다. 본 연구에서는 메모리 커럽션 취약점을 이용한 공격을 탐지하기 위한 기법 중 하나인 Multi-variant Execution Environment (MVEE)를 소개하고, MVEE의 핵심 요소 기술인 레플리카 생성 기법들에 대하여 설명하고 비교 분석하고자 한다.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.1-9
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• 2015

As attacks in cyber space become advanced and complex, monotonous defense approach of one-one matching manner between attack and defense may be limited to defend them. More efficient defense method is required. This paper proposes multi layers security scheme that can support to defend assets against diverse cyber attacks in systematical and adaptive. We model multi layers security scheme based on Defense Zone including several defense layers and also discuss essential technical elements necessary to realize multi layers security scheme such as cyber threats analysis and automated assignment of defense techniques. Also effects of multi layers security scheme and its applicability are explained. In future, for embodiment of multi layers security scheme, researches about detailed architecture design for Defense Zone, automated method to select the best defense technique against attack and modeling normal state of asset for attack detection are needed.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.257-262
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• 2023

Recently there are increasing number of claims that it is necessary to build a countermeasure in case of aggressive threats by small drones. During Russia-Ukraine war ignited by Russian invasion on February 2022, attacking drones have been being used widely to damage other country's national major facilities. On December 2022, 5 drones sent by North Korea made a flight around Seoul, South Korea about 7 hours, but it was not successful to search and track them. Furthermore, none of these were destroyed and shot down. Counter-drone system is essential system to search and identify unintended small drones and disable them. This paper is for proposing required functions for building a counter-drone system for national major facilities. We conducted focus group interviews with relevant government officials and analyzed their suggestions on how to augment protection capabilities to defend against small drone attacks.