• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1519-1530
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• 2020

As livestock farms grow in size, the number of breeding individuals increases, making it difficult to manage livestock. Livestock farms require an integrated management system such as a monitoring system, an access control system, and an abnormal behavior detection system to manage livestock houses. In this paper, a smart korean cattle livestock management system using IoT and AI technology was proposed for livestock management in livestock farms. The smart korean cattle farm management system consists of a monitoring and control system, a vehicle access management system, and an abnormal cattle behavior detection system. It is expected that this will help manage large-scale livestock houses, and additional research is needed to improve the performance of abnormal behavior detection in the future.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.1
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• pp.83-88
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• 2010

Acoustic telemetry is a useful method to investigate fish behavior and is widely used to obtain biological information. In this study, the detection ability of a mooring-type acoustic telemetry system and the seasonal changes were studied for survey design and data analysis. The system detection range was examined with an underwater noise model, and seasonal changes were estimated with a ray-tracing program and underwater temperature profile data. The field experiment was conducted with two sets of pingers and six receivers to estimate the difference in detection rate by installation depth and to compare the model estimate. Results indicated that the long-range detection ability of the acoustic telemetry system was significantly affected by underwater temperature. The detection rate rapidly decreased near the sea surface or bottom despite that the near-range Signal to noise ratio was sufficient.

• Journal of the Korean Chemical Society
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• v.63 no.1
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• pp.45-50
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• 2019

Because arsenic (As) is a chemical substance toxic to humans, there have been extensive investigations on the development of As detection methods. In this study, the electrochemical determination of As on nanoporous gold (NPG) electrodes was investigated using anodic stripping voltammetry. The electrochemical surface area of the NPG electrodes was controlled by changing the reaction times during the anodization of Au for NPG preparation, and its effect on the electrochemical behavior during As detection was examined. The detection efficiency of the NPG electrodes improved as the roughness factor of the NPG electrodes increased up to around 100. A further increase in the surface area of the NPG electrodes resulted in a decrease of the detection efficiency due to high background current levels. The most efficient As detection efficiency was obtained on the NPG electrodes prepared with an anodization time of 50 s. The effects of the detection parameters and of the Cu interference in As detection were investigated and the NPG electrode was compared to flat Au electrodes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.4
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• pp.785-796
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• 2012

In this paper, we propose an effective Behavior-based detection technique using the frequency of system calls to detect malicious code, when the number of training data is fewer than the number of properties on system calls. In this study, we collect the Native APIs which are Windows kernel data generated by running program code. Then we adopt the normalized freqeuncy of Native APIs as the basic properties. In addition, the basic properties are transformed to new properties by GLDA(Generalized Linear Discriminant Analysis) that is an effective method to discriminate between malicious code and normal code, although the number of training data is fewer than the number of properties. To detect the malicious code, kNN(k-Nearest Neighbor) classification, one of the bayesian classification technique, was used in this paper. We compared the proposed detection method with the other methods on collected Native APIs to verify efficiency of proposed method. It is presented that proposed detection method has a lower false positive rate than other methods on the threshold value when detection rate is 100%.

• Journal of Convergence Society for SMB
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• v.1 no.1
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• pp.75-83
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• 2011

Unified Modeling Language (UML) is widely accepted in industry and particularly UML State Machine Diagram is popular for describing the dynamic behavior of classes. This paper discusses deadlock detection of System using UML State Machine Diagram. Since a State Machine Diagram is used for indivisual class' behavior, all the State Machine Diagrams of the classes in the system are combined to make a big system-wide State Machine Diagram to describe system behavior. Generally this system-wide State Machine Diagram is very complex and contains invalid state and transitions. To make it a usable and valid State Machine Diagram, synchronization and externalization are applied. The reduced State Machine Diagram can be used for describing system behavior thus conventional model-checking technique can be applied. This paper shows how deadlock detection of system can be applied with simple examples. All the procedures can be automatically done in the tool.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.1-11
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• 2017

In this paper, we design and implement PADIL(Prediction And Detection of Information Leakage) system that predicts and detect information leakage behavior of insider by analyzing network traffic and applying a variety of machine learning methods. we defined the five-level information leakage model(Reconnaissance, Scanning, Access and Escalation, Exfiltration, Obfuscation) by referring to the cyber kill-chain model. In order to perform the machine learning for detecting information leakage, PADIL system extracts various features by analyzing the network traffic and extracts the behavioral features by comparing it with the personal profile information and extracts information leakage level features. We tested various machine learning methods and as a result, the DecisionTree algorithm showed excellent performance in information leakage detection and we showed that performance can be further improved by fine feature selection.

• Journal of KIISE
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• v.43 no.6
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• pp.718-723
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• 2016

In streaming data analysis, detecting concept drift accurately is important to maintain the performance of classification model. Error rates are usually used for concept drift detection. However, by describing prediction results with only binary values of 0 or 1, useful information about a behavior pattern of a classifier can be lost. In this paper, we propose an effective concept drift detection method which describes performance pattern of a classifier by utilizing probability estimates for class prediction and detects a significant change in a classifier behavior. Experimental results on synthetic and real streaming data show the efficiency of the proposed method for detecting the occurrence of concept drift.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.1-8
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• 2016

Nowadays, distributed denial of service (DDoS) attacks on web sites reward attackers financially or politically because our daily lifes tightly depends on web services such as on-line banking, e-mail, and e-commerce. One of DDoS attacks to web servers is called HTTP-GET flood attack which is becoming more serious. Most existing techniques are running on the application layer because these attack packets use legitimate network protocols and HTTP payloads; that is, network-level intrusion detection systems cannot distinguish legitimate HTTP-GET requests and malicious requests. In this paper, we propose a practical detection technique against HTTP-GET flood attacks, based on the access behavior of inline objects in a webpage using NetFlow data. In particular, our proposed scheme is working on the network layer without any application-specific deep packet inspections. We implement the proposed detection technique and evaluate the ability of attack detection on a simple test environment using NetBot attacker. Moreover, we also show that our approach must be applicable to real field by showing the test profile captured on a well-known e-commerce site. The results show that our technique can detect the HTTP-GET flood attack effectively.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.63-76
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• 2023

Identity and access management in cloud computing is one of the leading significant issues that require various security countermeasures to preserve user privacy. An authentication mechanism is a leading solution to authenticate and verify the identities of cloud users while accessing cloud applications. Building a secured and flexible authentication mechanism in a cloud computing platform is challenging. Authentication techniques can be combined with other security techniques such as intrusion detection systems to maintain a verifiable layer of security. In this paper, we provide an interactive, flexible, and reliable multi-factor authentication mechanisms that are primarily based on a proposed Authentication Method Selector (AMS) technique. The basic idea of AMS is to rely on the user's previous authentication information and user behavior which can be embedded with additional authentication methods according to the organization's requirements. In AMS, the administrator has the ability to add the appropriate authentication method based on the requirements of the organization. Based on these requirements, the administrator will activate and initialize the authentication method that has been added to the authentication pool. An intrusion detection component has been added to apply the users' location and users' default web browser feature. The AMS and intrusion detection components provide a security enhancement to increase the accuracy and efficiency of cloud user identity verification.