• Journal of Korean Society of Transportation
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• v.30 no.6
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• pp.71-80
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• 2012

This research suggested a real-time traffic signal control algorithm using individual vehicle travel times on an isolated signal intersection. To collect IDs and passing times from individual vehicles, space-based surveillance systems such as DSRC were adopted. This research developed models to estimate arrival flow rates, delays, and the change rate in delay, by using individual vehicle's travel time data. This real-time signal control algorithm could determine optimal traffic signal timings that minimize intersection delay, based on a linear programming. A micro simulation analysis using CORSIM and RUN TIME EXTENSION verified saturated intersection conditions, and determined the optimal traffic signal timings that minimize intersection delay. In addition, the performance of algorithm varying according to market penetration was examined. In spite of limited results from a specific scenario, this algorithm turned out to be effective as long as the probe rate exceeds 40 percent. Recently, space-based traffic surveillance systems are being installed by various projects, such as Hi-pass, Advanced Transportation Management System (ATMS) and Urban Transportation Information System (UTIS) in Korea. This research has an important significance in that the propose algorithm is a new methodology that accepts the space-based traffic surveillance system in real-time signal operations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4214-4223
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• 2012

The argument related to the use of real and anonymous names on the Internet bulletin board has recently become a main issue. When using real names, it is possible to violate free discussion and privacy. Also, when using anonymous names, it is possible to have the reverse function of the Internet in regard to the use of malicious replies or the distribution of false ideas. Therefore, this paper has made it possible to prevent the spread of the user's personal information and execute the single log-in process by using the XML-token method which is one of the SSO technologies. Also, by issuing virtual IDs and forming the path when establishing tokens, the anonymous bulletin board which provides anonymity with a conditional tracing process has been suggested. After analyzing the performance of visitor numbers at authentication time, the anonymous bulletin board based on the group signature method showed the average response rate of 0.72 seconds, 0.18 seconds, which was suggested scheme. In the authentication time 4-5 times faster response speed, respectively. Also, since the suggested system does not have to provide a single authentication process or make the user provide his or her signature, the level of user's convenience seems to be much higher. Such a result shows that the system suggested on the anonymous bulletin board has a more appropriate level of user's convenience.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• Smart Media Journal
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• v.12 no.7
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• pp.18-26
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• 2023

In this study, we propose a cow behavior recognition system based on YOLOX and OC-SORT. YOLO X detects targets in real-time and provides information on cow location and behavior. The OC-SORT module tracks cows in the video and assigns unique IDs. The quantitative analysis module analyzes the behavior and location information of cows. Experimental results show that our system demonstrates high accuracy and precision in target detection and tracking. The average precision (AP) of YOLOX was 82.2%, the average recall (AR) was 85.5%, the number of parameters was 54.15M, and the computation was 194.16GFLOPs. OC-SORT was able to maintain high-precision real-time target tracking in complex environments and occlusion situations. By analyzing changes in cow movement and frequency of mounting behavior, our system can help more accurately discern the estrus behavior of cows.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.355-364
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• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• The KIPS Transactions:PartC
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• v.10C no.2
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• pp.145-154
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• 2003

Current security efforts provided in such as firewall or IDS (intrusion detection system) of the network level suffer from many vulnerabilities in internal computing servers. Thus the necessity of secure OS is especially crucial in today's computing environment. This paper identifies secure OS requirements, analyzes tile research trends for secure Linux in terms of security kernel, and provides the descriptions of the multi-level security(MLS) Linux kernel which we have implemented. This security kernel-based Linux meets the minimum requirements for TCSEC Bl class as well providing anti-hacking, real-time audit trailing, restricting of root privileges, and enterprise suity management functions.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.507-516
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• 2001

In this paper, we implement sequence-based anomaly detection sensor using SOM and HMM, and analyze what is important information in system call and how a threshold is decided. The new filtering and reduction rules of SOM reduces the input size of HMM. This gives real-time processing to HMM-based anomaly detection sensor. Also, we introduced an anomaly count into the sensor. Due to lessened sensibility, a user easily understand easily the detection information and false-positive was decreased. And the active coordination of the threshold value makes the detection sensor adapt according to the system condition.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.4
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• pp.29-39
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• 2002

In general, the intrusion detection method of anomalous behaviors has high false alarm rate which contains false-positive and false-negative. To increase the sensitivity of intrusion detection, we propose a method of aggregate detection to reduce false alarm rate by using correlation between misuse activity detection sensors and anomalous ones. For each normal behavior and anomalous one, we produce the reflection rate between the result from one sensor and another in off-line. Then, we apply this rate to the result of real-time detection to reduce false alarm rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.71-79
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• 2009

Currently much research is being done on host based intrusion detection using system calls which is a portion of kernel based data. Sequence based and frequency based preprocessing methods are mostly used in research for intrusion detection using system calls. Due to the large amount of data and system call types, it requires a significant amount of preprocessing time. Therefore, it is difficult to implement real-time intrusion detection systems. Despite this disadvantage, the frequency based method which requires a relatively small amount of preprocessing time is usually used. This paper proposes an effective method for detecting denial of service attacks using the frequency based method. Principal Component Analysis(PCA) will be used to select the principle system calls and a bayesian network will be composed and the bayesian classifier will be used for the classification.

• Journal of IKEEE
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• v.16 no.3
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• pp.235-243
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• 2012

In this study, we developed two legged multi-joint robot by using wireless servo motor that was applied by wireless sensor network technology, which is widely used recently, and performed an experiment of walking method of two legged multi-joint robot. We constructed the star network with servo motors which were used at each joint of two-legged robot. And we designed the robot for operation by transmission of joint control signal from main control system or by transmission of the status of each joint to the main control system, so it operates with continuously checking the status of joints at same time. We developed the humanoid robot by using wireless digital servo motor which is different from existing servo motor control system, and controlled it by transmitting the information of angles and speeds of robot joints to the motor(node) as a feedback through main control system after connecting power and setting up the IDs to each joint. We solved noisy problem generated from wire and wire length to connection point of the control device by construction of the wireless network instead of using existing control method of wiring, and also solved problem of poor real time response to gait motion by controlling the position with continuous transmission of control signals to each joint. And we found that the effective control of robot is able by performing the simulation on walking motion in advance with the developed control algorithm which was downloaded into installed memory. Also we performed the stable walking with two-legged robot by attaching pressure sensor to robot sole. And we examined the robot gait operated by application of calculated algorithm on robot movement to each joint. In this study, we studied the method of controlling robot gait motion by using wireless servo motors and measured the torque applied to each joint, and found that the developed wireless servo motor by ZigBee sensor network offers easier control of two legged robot gait and better circuit configuration of it than the existing wired control system could do.