• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.186-189
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• 1993

능동 소나 체계에서 표적의 탐지거리 예측을 위하여 소나방정식이 이용되는데, 이는 음원 준위, 전달 손실, 표적 강도, 복반사 준위, 소음 준위, 방향성 이득, Detection threshold, Signal excess, 탐지 확률과 탐지거리의 요소로 구성된다. 본 연구에서는 능동 소나 체계에서 소나 깊이와 표적 깊이의 함수인 탐지거리를 계산하기 위한 알고리즘에 대해 살펴보았다. 소나의 각 요소와 환경이 주어졌을 때 SAFARI 모델을 이용하여 각 수신기의 깊이와 거리에서의 전달손실을 계산하였으며, 구하여진 전달 손실과 배경 소음 준위를 이용하여 Signal excess를 계산하였다. ROC(Receiver-operating-characteristic) 곡선을 이용하여 Signal excess를 탐지 확률로 계산한 후 두 항을 곱하여 각 깊이별 거리로 적분함으로서 탐지거리를 구하였다. 주파수 30Hz의 전방향 음원을 사용하여 여름의 일반적 음속 분포에서 계산한 결과 100m 음원 보다 300m 음원에서 상대적으로 큰 탐지거리를 얻었으며 각 음원 깊이별 평균 탐지거리는 100m 이하의 표면을 제외한 500m 까지는 거의 일정함을 알 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.208-218
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• 2006

In this paper, we introduce the creation methods of attack detection model using data mining technologies that can classify the latest attack types, and can detect the modification of existing attacks as well as the novel attacks. Also, we evaluate comparatively these attack detection models in the view of detection accuracy and detection time. As the important factors for creating detection models, there are data, attribute, and detection algorithm. Thus, we used NetFlow data gathered at the real network, and KDD Cup 1999 data for the experiment in large quantities. And for attribute selection, we used a heuristic method and a theoretical method using decision tree algorithm. We evaluate comparatively detection models using a single supervised/unsupervised data mining approach and a combined supervised data mining approach. As a result, although a combined supervised data mining approach required more modeling time, it had better detection rate. All models using data mining techniques could detect the attacks within 1 second, thus these approaches could prove the real-time detection. Also, our experimental results for anomaly detection showed that our approaches provided the detection possibility for novel attack, and especially SOM model provided the additional information about existing attack that is similar to novel attack.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1464-1480
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• 1997

Distributed systems offer environments for attaining high performance, fault-tolerance, information sharing, resource sharing, etc. But we cannot benefit from these potential advantages without suitable management of events/states occurring in distributed systems. These events and states can be symptoms for performance degradation, erroneous functions, suspicious activities, etc. and are subject to further analysis. To properly manage events/states, we need to be able to specify and efficiently detect these events/states. In this paper we first describe an event/state specification language and a centralized algorithm for detecting events/states specified with this language. Then we describe an algorithm for distributing an event/state detection task in a distributed system which is hierarchically organized. The algorithm consists of decomposing an event/state detection task into subtasks and allocation these subtasks to the proper nodes. We also explain a method to make the distributed detection fault-tolerant.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.6
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• pp.633-641
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• 2008

This study introduces the method of detecting and restoring occlusion areas by using epipolar algorithm and K-means classification algorithm for true ortho-photo generation. In the past, the techniques of detecting occlusion areas are using the reference images or information of buildings. But, in this study the occlusion areas can be automatically detected by using DTM data and exterior orientation parameters. The detected occlusion areas can be restored by using anther images or the computed values which are determined in K-means classification algorithm. In addition, this method takes advantages of applying epipolar algorithm in order to find same location in overlapping areas among images.

• Journal of the Korea Society of Computer and Information
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• v.7 no.1
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• pp.68-73
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• 2002

One widely-used technique by which network attackers attain anonymity and complicate their apprehension is by employing stepping stones: they launch attacks not from their own computer but from intermediary hosts that they previously compromised. We develop an efficient algorithm for detecting stepping stones by monitoring a site's Internet access link. The algorithm is based on the distinctive characteristics(packet size, timing) of interactive traffic, and not on connection contents, and hence can be used to find stepping stones even when the traffic is encrypted. We evaluate the algorithm on large Internet access traces and find that it Performs quite well. However, the success of the algorithm is tempered by the discovery that large sites have many users who routinely traverse stepping stones for a variety of legitimate reasons.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.147-155
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• 2016

This paper presents a jamming detection and suppression algorithm of a frequency-modulated continuous-wave(FMCW) radar altimeter. The radar altimeter measures the noise level at the noise measuring period before the transmitting and receiving period and finds the number of sampled noise data over the jamming threshold for detecting the jamming. For a jamming suppression technique, we design the time domain jamming suppression, transmit/receive power control and frequency hopping methods. To assess more realistic operation, the radar altimeter was performed a field test. Through the field test, we verified the algorithms successfully.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.109-118
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• 2020

Recently, people are spending a lot of time inside their homes because of various diseases. It is difficult to ask others for help in the case of a single-person household that is injured in the house or infected with a disease and needs help from others. In this study, an algorithm is proposed to detect emergency event, which are situations in which single-person households need help from others, such as injuries or disease infections, in their homes. It proposes vision pattern detection algorithms using home CCTVs, audio pattern detection algorithms using artificial intelligence speakers, activity pattern detection algorithms using acceleration sensors in smartphones, and dust pattern detection algorithms using air purifiers. However, if it is difficult to use due to security issues of home CCTVs, it proposes a fusion method combining audio, activity and dust pattern sensors. Each algorithm collected data through YouTube and experiments to measure accuracy.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.7-19
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• 2019

Ransomware is a malicious program that requires the cost of decryption after encrypting files on the user's desktop. Since the frequency and the financial damage of ransomware attacks are increasing each year, ransomware prevention, detection and recovery system are needed. Baek et al. proposed SSD-Insider, an algorithm for detecting ransomware within SSD. In this paper, we propose an AdvanSSD-Insider algorithm that substitutes a hash table used for the overwriting check with a bloom filter in the SSD-Insider. Experimental results show that the AdvanSSD-Insider algorithm reduces memory usage by up to 90% and execution time by up to 77% compared to the SSD-Insider algorithm and achieves the same detection accuracy. In addition, the AdvanSSD-Insider algorithm can monitor 10 times longer than the SSD-Insider algorithm in same memory condition. As a result, detection accuracy is increased for some ransomware which was difficult to detect using previous algorithm.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.34-40
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• 2001

This thesis shows that the edge-chasing deadlock detection algorithm of Choudhary fails to remove the existing deadlocks after committing the transaction whose priority is lowest on the transaction wait-fer path. A modified algorithm that solves this problem is proposed.