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

In this paper, we propose a secure communication framework for interaction and information sharing between a server and agents in DS-NVSA(Detection System of Network Vulnerability Scan Attacks) proposed in〔1〕. For the scalability and interoperability with other detection systems, we design the proposed IDMEF and IAP that have been drafted by IDWG. We adapt IDMEF and IAP to the proposed framework and provide SKTLS(Symmetric Key based Transport Layer Security Protocol) for the network environment that cannot afford to support public-key infrastructure. Our framework provides the reusability of heterogeneous intrusion detection systems and enables the scope of intrusion detection to be extended. Also it can be used as a framework for ESM(Enterprise Security Management) system.

• JSTS:Journal of Semiconductor Technology and Science
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• v.2 no.2
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• pp.141-146
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• 2002

The effect of the wide-bandgap Schottky barrier enhancement cap layer on the performance of metal-semiconductor-metal photodetectors (MSMPD's) is presented. Judged by the dc characteristics, no considerable increase in recombination loss of carriers is resulted by the incorporation of the cap layer. However, about 45% of the detection efficiency is lost for the cap-layered MSMPD's even with a graded layer incorporated under pulse operation, and it was found to be due mainly to the capturing and slow release of the photocarriers at the heterointerface. The loss mechanism of the pulse detection efficiency is believed to be responsible for the intersymbol interference and the increased bit-error-rate (BER) observed in MSMPD's when used with a high bit rate pseudo-random-bit-stream (PRBS) data pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.786-799
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• 2021

Road damage detection is important for road maintenance. With the development of deep learning, more and more road damage detection methods have been proposed, such as Fast R-CNN, Faster R-CNN, Mask R-CNN and RetinaNet. However, because shallow and deep layers cannot be extracted at the same time, the existing methods do not perform well in detecting objects with fewer samples. In addition, these methods cannot obtain a highly accurate detecting bounding box. This paper presents a Multi-level Feature Pyramids method based on M2det. Because the feature layer has multi-scale and multi-level architecture, the feature layer containing more information and obvious features can be extracted. Moreover, an attention mechanism is used to improve the accuracy of local boundary boxes in the dataset. Experimental results show that the proposed method is better than the current state-of-the-art methods.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.46-51
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• 2012

Transmit signal of Enhancement Layer in AT-DMB system is received by Coherent Detection, but in Base Layer of AT-DMB, a differential modulation and demodulation is adopted, same as the T-DMB. Especially for the coherent dectection of enhancement layer in AT-DMB system, a channel estimation must be employed. In this paper, I will show that the BER performance of Base-Layer in AT-DMB system will be improved by using the channel estimation information. The suggested method is focusing the constallations after Equalizaing to the nearlest ${\pi}$/4-shift DQPSK constallation points. Simulation results show that for the non-coding environment, the BER performance of AWGN channel, about 2-dB gain can be achieved at $10^{-4}BER$.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.92-98
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• 2019

In this paper, we propose a deep learning system based on morphological neural network(MNN). The deep learning layers are morphological operation layer, pooling layer, ReLU layer, and the fully connected layer. The operations used in morphological layer are erosion, dilation, and edge detection, etc. Unlike CNN, the number of hidden layers and kernels applied to each layer is limited in MNN. Because of the reduction of processing time and utility of VLSI chip design, it is possible to apply MNN to various mobile embedded systems. MNN performs the edge and shape detection operations with a limited number of kernels. Through experiments using database images, it is confirmed that MNN can be used as a deep learning system and its performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.139-147
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• 2014

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. The proposed dual-layer particle sampling (DLPS) algorithm consists of parent-particles (PP) in the first layer for detecting multiple objects and child-particles (CP) in the second layer for tracking objects. In the first layer, PPs detect persons using a classifier trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects, and efficiently reduce the processing time using the sampled particles based on motion distribution in video sequences.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.7
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• pp.63-70
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• 2004

This paper describes the link layer and physical layer of the Serial ATA which is the next generation for parallel ATA specification that defines data transfer between PC and peripheral storage devices. The link layer consists of CRC generation/error detection, 8b/10b decoding/encoding, primitive generation/detection block. For the physical layer, it includes CDR(Cock Data Recovery), transmission PLL, serializer/de-serializer. It also includes generation and receipt of OOB(Out-Of-Band) signal, impedance calibration, squelch circuit and comma detection/generation. Additionally, this chip includes TCB(Test Control Block) and BIST(Built-In Selt Test) block to ease debugging and verification. It is fabricated with 0.18${\mu}{\textrm}{m}$ standard CMOS cell library. All the function of the link layer operate properly. For the physical layer, all the blocks operate properly but the data transfer is limited to the 1.28Gbps. This is doe to the affection or parasitic elements and is verified with SPICE simulation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.39-52
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• 2011

An application-layer attack can effectively achieve its objective with a small amount of traffic, and detection is difficult because the traffic type is very similar to that of legitimate users. We have discovered a unique characteristic that is produced by a difference in client intention: Both a legitimate user and DDoS attacker establish a session through a 3-way handshake over the TCP/IP layer. After a connection is established, they request at least one HTTP service by a Get request packet. The legitimate HTTP user waits for the server's response. However, an attacker tries to terminate the existing session right after the Get request. These different actions can be interpreted as a difference in client intention. In this paper, we propose a detection algorithm for application layer DDoS attacks based on this difference. The proposed algorithm was simulated using traffic dump files that were taken from normal user networks and Botnet-based attack tools. The test results showed that the algorithm can detect an HTTP-Get flooding attack with almost zero false alarms.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.13-20
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• 2019

Recently, object detection technology based on CNN has been actively studied. Object detection technology is used as an important technology in autonomous vehicles, intelligent image analysis, and so on. In this paper, we propose a rotation change robust object detection system by applying MI-FL (Moment Invariant-Feature Layer) to SSD (Single Shot Multibox Detector) which is one of CNN-based object detectors. First, the features of the input image are extracted based on the VGG network. Then, a total of six feature layers are applied to generate bounding boxes by predicting the location and type of object. We then use the NMS algorithm to get the bounding box that is the most likely object. Once an object bounding box has been determined, the invariant moment feature of the corresponding region is extracted using MI-FL, and stored and learned in advance. In the detection process, it is possible to detect the rotated image more robust than the conventional method by using the previously stored moment invariant feature information. The performance improvement of about 4 ~ 5% was confirmed by comparing SSD with existing SSD and MI-FL.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.719-726
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• 2002

In this paper, we present a layered watermarking technique for digital audio data that is capable of detecting timing change and adapting complexity in detection. The proposed watermarking uses echo hiding as the first layer, which enables the detector to estimate linear speed change. The spread spectrum watermark is then inserted in the second layer which includes additional information like copyright data. We use two kinds of sequences in the second layer, one of which is for synchronization and the other is for data. The results of previous layer are used to make estimate of timing change in the next layer. The detector in the presented method can select detecting range form the first layer to the first layer, second pre-layer, or second main-layer due to the required system specification. Experimental results show that the proposed watermarking technique is robust to several processing attacks including timing change.