• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.264-272
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• 1998

This paper proposes a fault detection and recovery mechanism for a fault-tolerant Mini-MAP system, and provides detailed techniques for its implementation. This paper considers the fault-tolerant Mini-MAP system which has dual layer structure from the LLC sublayer down to the physical layer to cope with the faults of those layers. For a good fault detection, a redundant and hierarchical fault supervision architecture is proposed and its implementation technique for a stable detection operation is provided. Information for the fault location is provided from data reported with a fault detection and obtained by an additional network diagnosis. The faults are recovered by the stand-by sparing method applied for a dual network composed of two equivalent networks. A network switch mechanism is proposed to achieve a reliable and stable network function. A fault-tolerant Mini-MAP system is implemented by applying the proposed fault detection and recovery mechanism.

• 대한임베디드공학회논문지
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• 제12권6호
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• pp.395-403
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• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

• 융합보안논문지
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• 제12권1호
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• pp.55-61
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• 2012

현재 무선 이동 통신 기술은 빠른 속도로 발전되고 있으며 새로운 기술의 출현과 함께 빠르게 진화되고 있다. 이러한 발전을 기반으로 스마트폰의 이용자는 빠르게 늘어나고 있고 스마트 폰의 대중화에 따라 무선 네트워크의 사용은 쉽고 편리해졌다. 그러나 보안적인 관점에서 무선은 유선 네트워크에 비해 취약한 상태이며 본 논문에서 스마트폰을 활용한 모바일 AP 기술의 보안 취약점을 지적하였다. 즉, 사내 보안 기능을 갖춘 기업 망을 우회하여 기업 내의 정보를 외부로 유출하는 등의 기술로 악용 될 수 있다는 것이다. 이에 본 논문에서는 인증 네트워크 내에서 스마트 폰을 이용한 비인가 AP(Access Point)의 탐지 및 차단하는 기법에 대해 제안한다. 비 인가된 모바일 AP의 탐지는 리눅스 환경에서 개발한 탐지 프로그램을 사용하였으며 무선 센서를 통해 비 인가된 모바일 AP의 무선 패킷을 분석하였다. 또한 무선 센서를 통해 탐지된 비인가 모바일 AP는 무선 패킷에 정보를 분석하여 본 논문에서 제안하는 차단 기법을 통해 차단하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2512-2531
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• 2014

We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.

• 전기전자학회논문지
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• 제13권2호
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• pp.140-149
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• 2009

최근 인터넷을 통한 각종 침해사고 및 트래픽 폭주와 같은 현상이 급격하게 증가함에 따라 네트워크의 비정상적 상황을 조기에 탐지하기 위한 보다 능동적이고 진보적인 기술이 요구되고 있다. 본 논문에서는 캠퍼스 네트워크와 같이 트래픽이 주기적인 특성을 띠는 환경에서 Fisher 선형 분류법(FLD)을 사용하여 트래픽을 두 개의 그룹으로 분류하고, 네트워크에 유입되는 트래픽이 어떤 그룹에 속하는지를 판별하는 기법을 제안한다. 이를 위해 WISE-Mon이라 불리는 트래픽 분석 시스템을 개발하여 캠퍼스 네트워크의 트래픽을 수집하고 이를 모니터링해서 분석을 수행한다. 생성된 트래픽의 training set을 이용하여 비정상 트래픽의 범위를 판단하기 위한 chi-square distribution을 유도하고, FLD를 적용하여 유입되는 트래픽을 두 그룹으로 분리하기 위한 초평면 (hyperplane)을 만든다. 또한 네트워크 내의 트래픽 패턴이 시간이 지남에 따라 계속적으로 변하는 상황을 반영하기 위하여 self-learning 알고리즘을 적용한다. 캠퍼스 네트워크의 트래픽을 적용한 수학적 결과를 통하여 제안하는 기법의 정확성과 신뢰도를 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.4774-4796
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• 2018

Wireless sensor networks are composed of a large number of inexpensive and tiny sensors used in different areas including military, industry, agriculture, space, and environment. Fault tolerance, which is considered a challenging task in these networks, is defined as the ability of the system to offer an appropriate level of functionality in the event of failures. The present study proposed an intelligent throughput descent and distributed energy-efficient mechanism in order to improve fault tolerance of the system against soft and permanent faults. This mechanism includes determining the intelligent neighborhood radius threshold, the intelligent neighborhood nodes number threshold, customizing the base paper algorithm for distributed systems, redefining the base paper scenarios for failure detection procedure to predict network behavior when running into soft and permanent faults, and some cases have been described for handling failure exception procedures. The experimental results from simulation indicate that the proposed mechanism was able to improve network throughput, fault detection accuracy, reliability, and network lifetime with respect to the base paper.

• 대한산업공학회지
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• 제34권2호
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• pp.246-254
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• 2008

In this paper, a new efficient hidden node detection method is proposed to decide whether the RTS/CTS mechanism is necessary to resolve the hidden node problem for the data transmission of each node in infrastructure mode IEEE 802.11 wireless LANs. The nodes, for which the RTS/CTS mechanism is found to be not necessary by the hidden node detection method, can transmit their data frames without performing the RTS/CTS exchange. Only the nodes, for which the RTS/CTS mechanism is found to be necessary by the hidden node detection method, perform the RTS/CTS exchange before their data frame transmissions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1722-1741
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• 2017

Intrusion detection techniques based on virtual machine introspection (VMI) provide high temper-resistance in comparison with traditional in-host anti-virus tools. However, the presence of semantic gap also leads to the performance and compatibility problems. In order to map raw bits of hardware to meaningful information of virtual machine, detailed knowledge of different guest OS is required. In this work, we present VDSM, a lightweight and general approach based on driver separation mechanism: divide semantic view reconstruction into online driver of view generation and offline driver of semantics extraction. We have developed a prototype of VDSM and used it to do intrusion detection on 13 operation systems. The evaluation results show VDSM is effective and practical with a small performance overhead.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.358-361
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• 2021

In order to improve the accuracy of small target detection more effectively, this paper proposes an improved single shot detector (SSD) target detection and recognition method based on cspdarknet53, which introduces lightweight ECA attention mechanism and Feature Pyramid Network (FPN). First, the original SSD backbone network is replaced with cspdarknet53 to enhance the learning ability of the network. Then, a lightweight ECA attention mechanism is added to the basic convolution block to optimize the network. Finally, FPN is used to gradually fuse the multi-scale feature maps used for detection in the SSD from the deep to the shallow layers of the network to improve the positioning accuracy and classification accuracy of the network. Experiments show that the proposed target detection algorithm has better detection accuracy, and it improves the detection accuracy especially for small targets.

• Computers and Concrete
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• 제32권6호
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• pp.615-623
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• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.