• Journal of Communications and Networks
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• v.14 no.5
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• pp.518-523
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• 2012

This paper presents new coherent and non-coherent detection methods for the IEEE 802.15.4a low-rate ultra-wideband physical layer with forward error correction (FEC) coding techniques. The coherent detection method involving channel estimation is based on the correlation characteristics of the preamble signal. A coherent receiver uses novel iterated selective-rake (IT-SRAKE) to detect 2-bit data in a non-line-of-sight channel. The non-coherent detection method that does not involve channel estimation employs a 2-bit data detection scheme using modified transmitted reference pulse cluster (M-TRPC) methods. To compare the two schemes, we have designed an IT-SRAKE receiver and a MTRPC receiver using an IEEE 802.15.4a physical layer. Simulation results show the performance of IT-SRAKE is better than that of the M-TRPC by 3-9 dB.

• Journal of Microbiology and Biotechnology
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• v.16 no.8
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• pp.1169-1173
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• 2006

Imaging ellipsometry (IE) for detection of binding of Escherichia coli O157:H7 (E. coli O157:H7) to an immunosensor is reported. A protein G layer, chemically bound to a self-assembled layer of 11-mercaptoundecanoic acid (11-MUA), was adopted for immobilization of monoclonal antibody against E. coli O157:H7 (Mab). The immobilization of antibody was investigated using surface plasmon resonance. To fabricate antibody spots on a gold surface, protein G solution was spotted onto the gold surface modified with an 11-MUA layer, followed by immobilizing Mab on the protein G spot. Ellipsometric images of the protein G spot, the Mab spot, and Mab spots with binding of E. coli O157:H7 in various concentrations were acquired using the IE system. The change of mean optical intensity of the Mab spots in the ellipsometric images indicated that the lowest detection limit was $10^3$CFU/ml for E. coli O157:H7. Thus, IE can be applied to an immunosensor for detection of E. coli O157:H7 as a detection method with the advantages of allowing label-free detection, high sensitivity, and operational simplicity.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.127-134
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• 2011

Malicious botnet has been used for more malicious activities, such as DDoS attacks, sending spam messages, steal personal information, etc. To prevent this, many studies have been preceded. But malicious botnets have evolved and evaded detection systems. In particular, HTTP GET Request attack that exploits the vulnerability of the application layer is used. ALAB of ALADDIN proposed by ETRI is DDoS attack detection system that HTTP GET, Incomplete GET request flooding attack detection algorithm is applied. In this paper, we extend Incomplete GET detection algorithm of ALAB and derive the optimal configuration parameters to verify the validity of the algorithm ALAB by the study of the normal and attack packets.

• Bulletin of the Korean Chemical Society
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• v.32 no.6
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• pp.1975-1979
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• 2011

The modification of a gas diffusion layer (GDL), a vital component in polymer electrolyte fuel cells, is described here for use in the electrochemical detection of antibody-antigen biosensors. Compared to other substrates (gold foil and graphite), mouse anti-rHBsAg monoclonal antibody immobilized on gold-coated GDL (G-GDL) detected analytes of goat anti-mouse IgG antibody-ALP using a relatively low potential (-0.0021 V vs. Ag/AgCl 3 M NaCl), indicating that undesired by-reactions during electrochemical sensing should be avoided with G-GDL. The dependency of the signal against the concentration of analytes was observed, demonstrating the possibility of quantitative electrochemical biosensors based on G-GDL substrates. When a sandwich method was employed, target antigens of rHBsAg with a concentration as low as 500 ng/mL were clearly measured. The detection limit of rHBsAg was significantly improved to 10 ng/mL when higher concentrations of the 4-aminophenylphosphate monosodium salt (APP) acting on substrates were used for generating a redox-active product. Additionally, it was shown that a BSA blocking layer was essential in improving the detection limit in the G-GDL biosensor.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.688-701
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• 2023

The conventional methods of network intrusion detection system (NIDS) cannot measure the trend of intrusiondetection targets effectively, which lead to low detection accuracy. In this study, a NIDS method which based on a deep neural network in a big-data environment is proposed. Firstly, the entire framework of the NIDS model is constructed in two stages. Feature reduction and anomaly probability output are used at the core of the two stages. Subsequently, a convolutional neural network, which encompasses a down sampling layer and a characteristic extractor consist of a convolution layer, the correlation of inputs is realized by introducing bidirectional long short-term memory. Finally, after the convolution layer, a pooling layer is added to sample the required features according to different sampling rules, which promotes the overall performance of the NIDS model. The proposed NIDS method and three other methods are compared, and it is broken down under the conditions of the two databases through simulation experiments. The results demonstrate that the proposed model is superior to the other three methods of NIDS in two databases, in terms of precision, accuracy, F1- score, and recall, which are 91.64%, 93.35%, 92.25%, and 91.87%, respectively. The proposed algorithm is significant for improving the accuracy of NIDS.

• Journal of Microbiology and Biotechnology
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• v.12 no.5
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• pp.780-786
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• 2002

An immunosensor based on surface plasmon resonance (SPR) with a self-assembled protein G layer was developed for the detection of Escherichia coli O157:H7. A self-assembled protein C layer on a gold (Au) surface was fabricated by adsorbing the mixture of 11-mercaptoundecanoic acid (MUA) and hexanethiol at various molar ratios and by activating chemical binding between free amine (-$NH_2$) of protein G and 11-(MUA) using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDAC) in series. The formation of a self-assembled protein G layer on an Au substrate and the binding of the antibody and antigen in series were confirmed by SPR spectroscopy. The surface morphology analyses of the self-assembled protein G layer on the Au substrate, monoclonal antibody (Mab) against E. coli O157:H7 which was immobilized on protein G, and bound E. coli O157:H7 extracts on Immobilized Mab against E. coii O157:H7 were performed by atomic force microscopy (AFM). The detection limit of the SPR-based immunosensor for E. coli O157:H7 was found to be about $10^4$ cells/ml.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.444-452
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• 2024

This study proposes a deep learning architecture optimized for fire detection derived through Layer Importance Evaluation. In order to solve the problem of unnecessary complexity and operation of the existing Convolutional Neural Network (CNN)-based fire detection system, the operation of the inner layer of the model based on the weight and activation values was analyzed through the Layer Importance Evaluation technique, the layer with a high contribution to fire detection was identified, and the model was reconstructed only with the identified layer, and the performance indicators were compared and analyzed with the existing model. After learning the fire data using four transfer learning models: Xception, VGG19, ResNet, and EfficientNetB5, the Layer Importance Evaluation technique was applied to analyze the weight and activation value of each layer, and then a new model was constructed by selecting the top rank layers with the highest contribution. As a result of the study, it was confirmed that the implemented architecture maintains the same performance with parameters that are about 80% lighter than the existing model, and can contribute to increasing the efficiency of fire monitoring equipment by outputting the same performance in accuracy, loss, and confusion matrix indicators compared to conventional complex transfer learning models while having a learning speed of about 3 to 5 times faster.

• ETRI Journal
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• v.37 no.6
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• pp.1108-1119
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• 2015

Multicast communication of mobile ad hoc networks is vulnerable to internal attacks due to its routing structure and high scalability of its participants. Though existing intrusion detection systems (IDSs) act smartly to defend against attack strategies, adversaries also accordingly update their attacking plans intelligently so as to intervene in successful defending schemes. In our work, we present a novel indirect internal stealthy attack on a tree-based multicast routing protocol. Such an indirect stealthy attack intelligently makes neighbor nodes drop their routing-layer unicast control packets instead of processing or forwarding them. The adversary targets the collision avoidance mechanism of the Medium Access Control (MAC) protocol to indirectly affect the routing layer process. Simulation results show the success of this attacking strategy over the existing "stealthy attack in wireless ad hoc networks: detection and countermeasure (SADEC)" detection system. We design a cross-layer automata-based stealthy attack on multicast routing protocols (SAMRP) attacker detection system to identify and isolate the proposed attacker. NS-2 simulation and analytical results show the efficient performance, against an indirect internal stealthy attack, of SAMRP over the existing SADEC and BLM attacker detection systems.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.10
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• pp.815-819
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• 1998

The silicon-nirtide membrane structure for IR sensor was fabricated through the etching and the direct bonding. The PRO($PbTiO_3$ ) layer for a IR detection was coated on the membrane and its characteristics were measured. The a attack of PTO layer during the etching of silicon wafer as well as the thermal isolation of the IR detection layer were eliminated through the method of bonding/etching of silicon wafer. The surface roughness of the membrane was measured by AFM, the micro voids and the non-contacted area were inspected by the PTO layer were measured, too.

• Journal of the military operations research society of Korea
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• v.11 no.2
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• pp.6-14
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• 1985

The layered multi-barrier defense situation against penetrating enemy threat is analytically modeled towards minimizing the penetration probability. Each layer is characterized by probability of detection and probability of kill given detection. The two capabilities are assumed independent. Detection in a layer, however, affects detection performance in subsequent layers. The following three models were formulated and investigated: (1) 'Model A' permits increase of detection performance in only the next barrier, (2) 'Model B' permits the increase in all subsequent barriers linearly, and (3) 'Model C' expresses the increase in an asymptotic exponential way. The best and the worst barrier combinations are determined through model exercise and model performances are compared through sensitivity analysis for the 'intensification factor.'