• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1467-1480
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• 2016

This paper proposes "A Black Hole Detection Protocol Design based on a Mutual Authentication Scheme on VANET." It consists of the Mutual Authentication Scheme (MAS) that processes a Mutual Authentication by transferring messages among a Gateway Node, a Sensor Node, and a User Node and the Black Hole Detection Protocol (BHDP) which detects a Non-Authentication Node by using the Session Key computed in the MAS and a Black Hole by using the Broadcasting Table. Therefore, the MAS can reduce the operation count of hash functions more than the existing scheme and protect a privacy from an eavesdropping attack and an information exposure by hashing a nonce and user's ID and password. In addition, the MAS prevents a replay attack by using the randomly generated nonce and the time stamp. The BHDP improves Packet Delivery ratio and Throughput more than the AODV with Black hole by 4.79% and 38.28Kbps. Also, it improves Packet Delivery ratio and Throughput more than the IDSAODV by 1.53% and 10.45Kbps. Hence it makes VANET more safe and reliable.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2086-2098
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• 2018

Security plays a vital role and is the key challenge in Mobile Ad-hoc Networks (MANET). Infrastructure-less nature of MANET makes it arduous to envisage the genre of topology. Due to its inexhaustible access, information disseminated by roaming nodes to other nodes is susceptible to many hazardous attacks. Intrusion Detection and Prevention System (IDPS) is undoubtedly a defense structure to address threats in MANET. Many IDPS methods have been developed to ascertain the exceptional behavior in these networks. Key issue in such IDPS is lack of fast self-organized learning engine that facilitates comprehensive situation awareness for optimum decision making. Proposed "Intelligent Behavioral Hybridized Intrusion Detection and Prevention System (IBH_IDPS)" is built with computational intelligence to detect complex multistage attacks making the system robust and reliable. The System comprises of an Intelligent Client Agent and a Smart Server empowered with fuzzy inference rule-based service engine to ensure confidentiality and integrity of network. Distributed Intelligent Client Agents incorporated with centralized Smart Server makes it capable of analyzing and categorizing unethical incidents appropriately through unsupervised learning mechanism. Experimental analysis proves the proposed model is highly attack resistant, reliable and secure on devices and shows promising gains with assured delivery ratio, low end-to-end delay compared to existing approach.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.173-180
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• 2008

In this paper, we proposed an algorithm for an effective public key and private key generation to implement a secure asymmetric watermarking system against the public key attack. The public key and private key generation is based on the linear transformation using a special matrix and the keys are designed to be able to have high correlation value. We also proposed a counter plan of public key attack. This method uses a multiple public key generation and distribution. As the results, the correlation value between the public key and the private key is high in the watermarked image. After the public key attack. this can detect the correlation by using other public key.

• Journal of Microbiology and Biotechnology
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• v.25 no.10
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• pp.1621-1628
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• 2015

Chlamydia possesses a conserved 7.5 kb plasmid that is known to play an important role in chlamydial pathogenesis, since some chlamydial organisms lacking the plasmid are attenuated. The chlamydial transformation system developed recently required the use of plasmid-free organisms. Thus, the generation and identification of plasmid-free organisms represent a key step in understanding chlamydial pathogenic mechanisms. A tricolor immunofluorescence assay for simultaneously detecting the plasmid-encoded Pgp3 and whole organisms plus DNA staining was used to screen C. muridarum organisms selected with novobiocin. PCR was used to detect the plasmid genes. Next-generation sequencing was then used to sequence the genomes of plasmid-free C. muridarum candidates and the parental C. muridarum Nigg strain. We generated five independent clones of plasmid-free C. muridarum organisms by using a combination of novobiocin treatment and screening plaque-purified clones with anti-Pgp3 antibody. The clones were confirmed to lack plasmid genes by PCR analysis. No GlgA protein or glycogen accumulation was detected in cells infected with the plasmid-free clones. More importantly, whole-genome sequencing characterization of the plasmid-free C. muridarum organism and the parental C. muridarum Nigg strain revealed no additional mutations other than loss of the plasmid in the plasmid-free C. muridarum organism. Thus, the Pgp3-based immunofluorescence assay has allowed us to identify authentic plasmid-free organisms that are useful for further investigating chlamydial pathogenic mechanisms.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.607-611
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• 2008

This paper proposes new video signature using spatio-temporal information for copy detection. The proposed video copy detection method is based on concentric circle partitioning method for each key frame. Firstly, key frames are extracted from whole video using temporal bilinear interpolation periodically and each frame is partitioned as a shape of concentric circle. For the partitioned sub-regions, 4 feature distributions of average intensity, its difference, symmetric difference and circular difference distributions are obtained by using the relation between the sub-regions. Finally these feature distributions are converted into binary signature by using simple hash function and merged together. For the proposed video signature, the similarity distance is calculated by simple Hamming distance so that its matching speed is very fast. From experiment results, the proposed method shows high detection success ratio of average 97.4% for various modifications. Therefore it is expected that the proposed method can be utilized for video copy detection widely.

• Journal of Sensor Science and Technology
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• v.26 no.6
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• pp.432-437
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• 2017

As 4.0 industry has been developed, research on a self-driving car technology and related parts of an automobile has been highly investigated recently. Particularly, a TAS(Torque Angle Sensor) module on steering wheel system has been considered as a key technology because of its precise angle, torque detection and high speed signal processing. The environmental assessment is generally required on the TAS module to examine high resolution of angle/torque detection. In the case of existing TAS module, angle detection errors has been occurred by back-lash on main and sub gear in addition to complicated structure caused by gears. In this paper, a structure of the TAS module, which minimizes the numbers of components and angle detection errors on the module compared with the existing TAS module, for vehicle steering system based on a Multi-track Encoder has been proposed. Also, angle detection signal processing board, and key technology of the TAS module were fabricated and evaluated. As a result of the experiments, we confirmed an excellent performance of the fabricated signal processing board for angle detection and an applicability of the fabricated angle detection board on the TAS module of vehicles by the environmental assessment an automobile standard.

• Journal of Microbiology and Biotechnology
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• v.33 no.8
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• pp.1101-1110
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• 2023

Streptococcus mutans is the primary causative agent of caries, which is one of the most common human diseases. Thus, rapid and early detection of cariogenic bacteria is critical for its prevention. This study investigated the combination of loop-mediated isothermal amplification (LAMP) and microfluid technology to quantitatively detect S. mutans. A low-cost, rapid microfluidic chip using LAMP technology was developed to amplify and detect bacteria at 2.2-2.2 × 10⁶ colony-forming units (CFU)/ml and its detection limits were compared to those of standard polymerase chain reaction. A visualization system was established to quantitatively determine the experimental results, and a functional relationship between the bacterial concentration and quantitative results was established. The detection limit of S. mutans using this microfluidic chip was 2.2 CFU/ml, which was lower than that of the standard approach. After quantification, the experimental results showed a good linear relationship with the concentration of S. mutans, thereby confirming the effectiveness and accuracy of the custom-made integrated LAMP microfluidic system for the detection of S. mutans. The microfluidic system described herein may represent a promising simple detection method for the specific and rapid testing of individuals at risk of caries.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.243-249
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• 2020

Event detection in wireless sensor networks is a key requirement in many applications. Acoustic sensors are one of the most frequently used sensors for event detection in sensor networks, but they are sensitive and difficult to handle because they vary greatly depending on the environment and target characteristics of the sensor field. In this paper, we propose a learning-based improvement of CFAR algorithm for increasing node-level event detection performance in acoustic sensor networks, and verify the effectiveness of the designed algorithm by comparing and evaluating the event detection performance with other algorithms. Our experimental results demonstrate the superiority of the proposed algorithm by increasing the detection accuracy by more than 45.16% by significantly reducing false positives by 7.97 times while slightly increasing the false negative compared to the existing algorithm.

• ETRI Journal
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• v.37 no.3
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.