• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.48-57
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• 2010

Previous post sender recognition study was focused on recognizing the address of receiver. Relatively, there was lack of study to recognize the information of sender's address. Post sender recognition study is necessary for the service and application using sender information such as returning. This paper did the experiment and suggested how to recognize post sender using SIFT. Although SIFT shows great recognition rate, SIFT had problems with time and mis-recognition. One is increased time to match keypoints in proportion as the number of registered model. The other is mis-recognition of many similar keypoints even though they are all different models due to the nature of post sender. To solve the problem, this paper suggested SIFT adding distance function and did the experiment to compare time and function. In addition, it is suggested how to register and classify models automatically without the manual process of registering models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.35-40
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• 2013

The messages on mobile terminal may contain context information such as user's location, time and preference. This paper proposes message forwarding system based on user's context. The server stores the predefined target addresses which may be mobile phone number, web server address and email address. On request for message transmission, the server tries to find a target address matching a user's context. Thus, a sender transmits messages to a representative address without knowing various receiver's addresses and a receiver can retrieve classified messages according to subject/location.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.169-172
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• 2010

본 논문에서는 기존의 ISODATA 알고리즘을 네트워크 공격탐지에 더욱 적합하도록 개선하여 공격을 탐지하는 새로운 방법을 제안한다. 수많은 인터넷상의 트래픽 정보들을 군집화하여 유사도를 비교하는 방법을 통해 공격을 판단한다. 기본적인 절차는 송신자 IP와 Port, 수신자 IP와 Port 정보를 이용하여 송신자와 수신자 사이의 관계를 분석하고 그 특징 값들을 이용하여 개선된 군집화 알고리즘을 이용하여 군집화를 수행한다. 그리고 얻어진 패턴의 특징값을 인공신경망에 학습하여 공격유형을 분류하고 탐지하도록 한다. 기존의 공격탐지 방법과 비교했을 때, 계산양이 적고 속도가 빠르다는 장점이 있으며 제안하는 방법의 우수성을 실험을 통해 증명하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1942-1950
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• 2013

This paper suggests a new method of detecting attacks of network traffic by visualizing original traffic data and applying multi-class SVM (support vector machine). The proposed method first generates 2D images from IP and ports of transmitters and receivers, and extracts linear patterns and high intensity values from the images, representing traffic attacks. It then obtains variance of ports of transmitters and receivers and extracts the number of clusters and entropy features using ISODATA algorithm. Finally, it determines through multi-class SVM if the traffic data contain DDoS, DoS, Internet worm, or port scans. Experimental results show that the suggested multi-class SVM-based algorithm can more effectively detect network traffic attacks.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.