• Structural Engineering and Mechanics
• /
• v.25 no.2
• /
• pp.131-146
• /
• 2007

This paper presents the results of a study to show how the development of a crack alters the structural behavior of I-shaped steel beams and how this can be used to evaluate nondestructive evaluation techniques. The approach is based on changes in the dynamic behavior. An approximate finite element model for a cracked beam with I-shaped cross-section is developed based on a simplified fracture model. The model is then used to review different damage cases. Damage detection techniques are studied to determine their ability to identify the existence of the crack and to identify its location. The techniques studied are the coordinate modal assurance criterion, the modal flexibility, and the state and the slope arrays.

• Journal of Mechanical Science and Technology
• /
• v.14 no.7
• /
• pp.722-729
• /
• 2000

The goal of this paper is to describe an advanced method of a crack detection: a new way to localize position and to estimate depth of a crack on rotating shaft. As a first step, the shaft is physically modelled with a finite element method and the dynamic mathematical model is derived using the Hamilton principle; thus, the system is represented by various subsystems. The equations of motion of the shaft with a crack are established by adapting the local stiffness change through breathing and gaping from the crack to an undamaged shaft. This is the reference system for the given system. Based on a model for transient behavior induced from vibration measured at the bearings, a nonlinear state observer is designed to detect cracks on the shaft. This is the elementary NL-observer (Beo). Using the observer, an Estimator (Observer Bank) is established and arranged at the certain position on the shaft. When a crack position is localized, the procedure for estimating of the depth is engaged.

• Journal of Korea Multimedia Society
• /
• v.21 no.3
• /
• pp.348-357
• /
• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.5
• /
• pp.1332-1341
• /
• 1999

Rapid expansion of network and increment of computer system access cause computer security to be an important issue. Hence, the researches in intrusion detection system(IDS)are active to reduce the risk from hackers. Considering IDS, we propose a new IDS model(DABIDS : Distributed Agent Based Intelligent intrusion Detection System) based on distributed intrusion detecting agents. The DABIDS dynamically collects intrusion behavior knowledge from each agents when some doubtable behaviors of users are detected and make new agents codes using intrusion scenario data base, and broadcast the detector codes to the distributed intrusion detecting agent of all node. This DABIDS can efficiently solve the problem to reduce the overhead for training detecting agent for intrusion behavior patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1722-1737
• /
• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.9
• /
• pp.4684-4705
• /
• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.11
• /
• pp.1017-1021
• /
• 2013

The traffic signal has been widely used in the transport system with a fixed time interval currently. This kind of setting time was determined based on experience for vehicles to generate a waiting time while allowing pedestrians crossing the street. However, this strict setting causes inefficient problems in terms of economic and safety crossing. In this research, we propose a monitoring algorithm to detect, track and check pedestrian crossing the crosswalk by the patterns of behavior. This monitoring system ensures the safety for pedestrian and keeps the traffic flow in efficient. In this algorithm, pedestrians are detected by using HOG feature which is robust to illumination changes in outdoor environment. According to a complex computation, the parallel process with the GPU as well as CPU is adopted for real-time processing. Therefore, pedestrians are tracked by the relationship of hue channel in image sequence according to the predefined pedestrian zone. Finally, the system checks the pedestrians' crossing on the crosswalk by its HOG based behavior patterns. In experiments, the parallel processing by both GPU and CPU was performed so that the result reaches 16 FPS (Frame Per Second). The accuracy of detection and tracking was 93.7% and 91.2%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.2
• /
• pp.766-783
• /
• 2012

Recently, many malicious users have attacked web browsers using JavaScript code that can execute dynamic actions within the browsers. By forcing the browser to execute malicious JavaScript code, the attackers can steal personal information stored in the system, allow malware program downloads in the client's system, and so on. In order to reduce damage, malicious web pages must be located prior to general users accessing the infected pages. In this paper, a novel framework (JsSandbox) that can monitor and analyze the behavior of malicious JavaScript code using internal function hooking (IFH) is proposed. IFH is defined as the hooking of all functions in the modules using the debug information and extracting the parameter values. The use of IFH enables the monitoring of functions that API hooking cannot. JsSandbox was implemented based on a debugger engine, and some features were applied to detect and analyze malicious JavaScript code: detection of obfuscation, deobfuscation of the obfuscated string, detection of URLs related to redirection, and detection of exploit codes. Then, the proposed framework was analyzed for specific features, and the results demonstrate that JsSandbox can be applied to the analysis of the behavior of malicious web pages.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.1
• /
• pp.73-78
• /
• 2016

In wireless sensor networks, sensors have capabilities of sensing and wireless communication, computing power and collect data such as sound, movement, vibration. Sensors need to communicate wirelessly to send their sensing data to other sensors or the base station and so they are vulnerable to many attacks like garbage packet injection that cannot be prevented by using traditional cryptographic mechanisms. To defend against such attacks, a behavior-based attack detection is used in which some specialized monitoring nodes overhear the communications of their neighbors(normal nodes) to detect illegitimate behaviors. It is desirable that the total sensing area of normal nodes covered by monitoring nodes is as large as possible. The previous researches have focused on selecting the monitoring nodes so as to maximize the number of normal nodes(node coverage), which does not guarantee that the area sensed by the selected normal nodes is maximized. In this study, we have developed an algorithm for selecting the monitoring nodes needed to cover the maximum sensing area. We also have compared experimentally the covered sensing areas computed by our algorithm and the node coverage algorithm.

• Convergence Security Journal
• /
• v.13 no.4
• /
• pp.109-115
• /
• 2013

Security threats through e-mail service, a representative tool to convey information on the internet, are on the sharp rise. The security threats are made in the path where malicious codes are inserted into documents files attached and infect users' systems by taking advantage of the weak points of relevant application programs. Therefore, to block infection of camouflaged malicious codes in the course of file transfer, this work proposed an integrity-checking and behavior-based detection system using File Transfer System (FTS), logical network partition, and conducted a comparison analysis with the conventional security techniques.