• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.965-983
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• 2014

Concurrent behaviors of multithreaded programs cannot be described effectively by automata-based models. Thus, concurrent program intrusion attempts cannot be detected. To address this problem, we proposed the process algebra-based detection model for multithreaded programs (PADMP). We generate process expressions by static binary code analysis. We then add concurrency operators to process expressions and propose a model construction algorithm based on process algebra. We also present a definition of process equivalence and behavior detection rules. Experiments demonstrate that the proposed method can accurately detect errors in multithreaded programs and has linear space-time complexity. The proposed method provides effective support for concurrent behavior modeling and detection.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.388-388
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• 2000

The complexity of nonlinear systems makes it difficult to ascertain their behavior using classical methods of analysis. Many efforts have been focused on the advanced algorithms and techniques that hold the promise of improving real-time optimal control while at the same time providing higher accuracy. In this paper, a fuzzy cell mapping method of real-time optimal control far nonlinear dynamical systems is proposed. This approach combines fuzzy logic with cell mapping techniques in order to find the optimal input level and optimal time interval in the finite set which change the state of a system to achieve a desired obiective. In order to illustrate this method, we analyze the behavior of an inverted pendulum using fuzzy cell mapping.

• Structural Engineering and Mechanics
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• v.54 no.5
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• pp.877-890
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• 2015

This paper aims to fill the technical gap on the elastic buckling behavior of functionally graded material (FGM) grid systems under inplane loads on which few research has been done. Material properties of an FG beam are assumed to vary smoothly in the thickness direction according to power and exponential laws. Based on a hybrid-stress finite element formulation, buckling solutions for FGM grid systems consisting of various aspect ratios and material gradation are provided. The numerical results demonstrate that the aspect ratio and material gradation play an important role in the buckling behavior of FGM grid systems. We believe that the new results obtained from this study, will be very useful to designers and researchers in this field.

• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.397-404
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• 2003

Intrusion detection techniques based on program behavior can detect potential intrusions against systems by analyzing system calls made by demon programs or root-privileged programs and building program profiles. But there is a drawback : large profiles must be built for each program. In this paper, we apply $X^2$ distance-based multivariate analysis to profiling program behavior and detecting abnormal behavior in order to reduce profiles. Experiment results show that profiles are relatively small and the detection rate is significant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2191-2203
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• 2011

In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.5
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• pp.69-78
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• 2004

The change of attack techniques paradigm was begun by fast extension of the latest Internet and new attack form appearing. But, Most intrusion detection systems detect only known attack type as IDS is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, the experiments to apply various techniques of anomaly detection are appearing. In this paper, we propose an behavior profiling method using Bayesian framework based on graphics from audit data and visualize behavior profile to detect/analyze anomaly behavior. We achieve simulation to translate host/network audit data into BF-XML which is behavior profile of semi-structured data type for anomaly detection and to visualize BF-XML as SVG.

• The Journal of Information Systems
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• v.25 no.3
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• pp.1-30
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• 2016

Purpose The purpose of this study is to grasp the factors influencing domestic SNS users' privacy protection behavior and verify their relationship through self-efficacy and responsiveness. Thus, this study tries to suggest efficient and effective measures for SNS personal information protection. Design/methodology/approach To this end, with main variables of the protection motivation theory based on the assumption that when users are exposed to the threat to their health, they would have protection motivation and change their behavior of protecting their health, a research model was suggested. In addition, in order to empirically verify the research model, a survey was performed targeting general college students having the experience of using SNS. Findings As a result of the analysis, first, perceived effectiveness and self-efficacy had a positive effect on responsiveness. Second, perceived barrier had a positive effect on self-efficacy. Third, self-efficacy and responsiveness had a positive effect on privacy protection behavior. This study is expected to contribute to establishing an effective guideline for measures that could induce SNS users' privacy protection behavior.

• Journal of Navigation and Port Research
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• v.31 no.4
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• pp.281-287
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• 2007

Human factors have been considered the primary reason of marine accidents. Especially, the collision between vessels is mostly mused by human behavior. However, there have not been many researches to clarify the reason of marine accidents mused by human factors quantitatively. In order to understand human factors and to enhance safe navigation systematically, using a full mission ship-handling simulator, we've investigated the characteristics of avoiding behavior taken by mariners. Further in order to apply the characteristics more widely and effectively, it's necessary to formulate the standard behavior for ship-handling in the condition of collision avoidance. Is this study, therefore, we intended to propose the concept to model the mariner's standard behavior on the handling of collision avoidance as the first step. As a result, we confirmed the contents of information processing in ship-handling that mariner's generally taking to avoid collision.

• Structural Engineering and Mechanics
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• v.83 no.6
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• pp.709-727
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• 2022

Fastening systems have a significant role in the response of railway slab track systems. Although experimental tests indicate nonlinear behavior of fastening systems, they have been simulated as a linear spring-dashpot element in the available literature. In this paper, the influence of the nonlinear behavior of fastening systems on the slab track response was investigated. In this regard, a nonlinear model of vehicle/slab track interaction, including two commonly used fastening systems (i.e., RFFS and RWFS), was developed. The time history of excitation frequency of the fastening system was derived using the short time Fourier transform. The model was validated, using the results of a comprehensive field test carried out in this study. The frequency response of the track was studied to evaluate the effect of excitation frequency on the railway track response. The results obtained from the model were compared with those of the conventional linear model of vehicle/slab track interaction. The effects of vehicle speed, axle load, pad stiffness, fastening preload on the difference between the outputs obtained from the linear and nonlinear models were investigated through a parametric study. It was shown that the difference between the results obtained from linear and nonlinear models is up to 38 and 18 percent for RWFS and RFFS, respectively. Based on the outcomes obtained, a nonlinear to linear correction factor as a function of vehicle speed, vehicle axle load, pad stiffness and preload was derived. It was shown that consideration of the correction factor compensates the errors caused by the assumption of linear behavior for the fastening systems in the currently used vehicle track interaction models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.