• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.4
• /
• pp.1307-1323
• /
• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.95-103
• /
• 2019

The number of one person households has grown steadily over the recent past and the population of lonely and unnoticed death are also observed. The phenomenon of one person households has been occurred. In the dark side of society, the remarkable number of lonely and unnoticed death are reported among different age-groups. We propose an unusual event detection method which may give a remarkable solution to reduce the number of the death rete for people dying alone and remaining undiscovered for a long period of time. The unusual event detection method we suggested to identify abnormal user behavior in their lives using vision pattern, audio pattern, and dust pattern algorithms. Individually proposed pattern algorithms have disadvantages of not being able to detect when they leave the coverage area. We utilized a fusion method to improve the accuracy performance of each pattern algorithm and evaluated the technique with multiple user behavior patterns in indoor areas.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1181-1191
• /
• 2021

The pandemic has rapidly developed a non-face-to-face environment. However, the sudden transition to a non-face-to-face environment has led to new security issues in various areas. One of the new security issues is the security threat of insiders, and the zero trust security model is drawing attention again as a technology to defend against it.. Software Defined Perimeter (SDP) technology consists of various security factors, of which device validation is a technology that can realize zerotrust by monitoring insider usage behavior. But the current SDP specification does not provide a technology that can perform device validation.. Therefore, this paper proposes a device validation technology using SVDD-based abnormal behavior detection technology through user behavior monitoring in an SDP environment and presents a way to perform the device validation technology in the SDP environment by conducting performance evaluation.

• Journal of Animal Science and Technology
• /
• v.64 no.6
• /
• pp.1237-1244
• /
• 2022

Horse breeders suffer massive economic losses due to dystocia, abortion, and stillbirths. In Thoroughbred mares, breeders often miss the foaling process because approximately 86% of the foaling events occur from 19:00 to 7:00; consequently, breeders cannot assist mares experiencing dystocia. To solve this problem, various foaling alarm systems have been developed. However, there is a need to develop a new system to overcome the shortcomings of the existing devices and improve their accuracy. To this end, the present study aimed to (1) develop a novel foaling alarm system and (2) compare its accuracy with that of the existing Foalert^TM system. Specifically, eighteen Thoroughbred mares (11.9 ± 4.0 years old) were included. An accelerometer was used to analyze specific foaling behaviors. Behavioral data were transmitted to a data server every second. Depending on the acceleration value, behaviors were automatically classified by the server as categorized behaviors 1 (behaviors without change in body rotation), 2 (behaviors with sudden change in body rotation, such as rolling over), and 3 (behaviors with long-term change in body rotation, such as lying down laterally). The system was designed to alarm when the duration of categorized behaviors 2 and 3 was 12.9% and that of categorized behavior 3 was 1% during 10 min. The system measured the duration of each categorized behavior every 10 min and transmitted an alarm to the breeders when foaling was detected. To confirm its accuracy, the foaling detection time of the novel system was compared with that of Foalert^TM. The novel foaling alarm system and Foalert^TM alarmed foaling onset respectively 32.6 ± 17.9 and 8.6 ± 1.0 min prior to foal discharge, and the foaling detection rate of both systems was 94.4%. Therefore, the novel foaling alarm system equipped with an accelerometer can precisely detect and alert foaling onset.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.2
• /
• pp.92-98
• /
• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.6
• /
• pp.1153-1166
• /
• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• Research in Community and Public Health Nursing
• /
• v.30 no.4
• /
• pp.471-483
• /
• 2019

Purpose: The purpose of this study was to identify factors associated with the intention of the prostate cancer screening (PCS). To achieve this purpose, a structural equation model was established based on the health belief model and the theory of planned behavior. Methods: The subjects of this study were 260 male participants who were between 40 and 74 years old and had not taken the PCS. Data were collected using a structured self-report questionnaire (i.e., perceived benefits, perceived barriers, attitude, subjective norms, perceived behavior control, and intention of the PCS). Descriptive statistics, reliability analysis, correlation analysis, confirmatory factor analysis, and fitness test were used to test hypotheses. Results: The intention of the PCS was directly affected by the perceived behavior control and indirectly influenced by the perceived benefits. The structural equation model also showed that the perceived behavior control explained 78% of the intention. Conclusion: To raise the intention to take the PCS, it is necessary to increase the confidence of a subject that may control its difficulties and inform the perceived benefits of the PCS to a subject.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.196-204
• /
• 2017

Behavior of intelligent video surveillance system is recognized by analyzing the pattern of the object of interest by using the frame information of video inputted from the camera and analyzes the behavior. Detection of object's certain behaviors in the crowd has become a critical problem because in the event of terror strikes. Recognition of object's certain behaviors is an important but difficult problem in the area of computer vision. As the realization of big data utilizing machine learning, data mining techniques, the amount of video through the CCTV, Smart-phone and Drone's video has increased dramatically. In this paper, we propose a multiple-sliding window method to recognize the cumulative change as one piece in order to improve the accuracy of the recognition. The experimental results demonstrated the method was robust and efficient learning units in the classification of certain behaviors.

• Clinical and Experimental Pediatrics
• /
• v.59 no.10
• /
• pp.414-420
• /
• 2016

Purpose: We investigated behavioral problems, attention problems, and cognitive function in children and adolescents born small for gestational age (SGA). Methods: Forty-six SGA children born at term and 46 appropriate for gestational age (AGA) children born at term were compared. Psychiatric symptoms were examined with reference to the Korean-Child Behavior Checklist, Korean-Youth Self Report, and Attention Deficit Hyperactivity Disorder Rating Scale (ADHD-RS). Cognitive function was estimated using the Wechsler Intelligence Scale. Sociodemographic data were recorded from interviews. Results: SGA children had high scores on delinquent behavior, aggressive behavior, and the externalizing scale, and they also showed a propensity for anxiety and depression. The SGA group had a higher mean ADHD-RS score than the AGA group ($10.52{\pm}8.10$ vs.$9.93{\pm}7.23$), but the difference was not significant. The SGA group had a significantly lower verbal intelligence quotient (IQ) than the AGA group, but the mean scores of both groups were within normal limits. Conclusion: This study indicates marked behavioral problems, such as delinquency, aggressiveness, and anxiety and depression, as well as low verbal IQ in the SGA group than in the AGA group. Even in cases in which these symptoms are not severe, early detection and proper treatment can help these children adapt to society.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.46 no.3
• /
• pp.232-238
• /
• 2010

The moving ranges and behavior of four wild abalones, Haliotis discus hannai, were measured by an acoustic telemetry technique. The shape of the sea bottom of the experimental area was surveyed by a bathymetry system and three self-recording type acoustic receivers were used for monitoring the behavior and measuring the movement range. The abalones (WA1-WA4) attached acoustic tags were released and measured the movement during ten months. Three abalones (WA1, WA3 and WA4) were successively detected around the released point during the experiment and were moved to the V2 area where water depth is deeper than the V1 area. The change of inhabitation depth was also detected from the depth sensor of WA4. As the result, abalones were moved to deeper water area accordance with the decrease of the water temperature. The moved ranges of abalones were approximately 200 - 400m from the release point.