• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.137-150
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• 2012

For social demand and technological development, systematic private information management and security guidance have been enhanced; however, the issue of leakage and invasion of private information is shown in many ways. In the management of such private information, the issue of how to protect such information is one of the sensitive key elements. As a criterion to decide the management policy of each property information consisting of private information, this article suggests Dynamic-Security-Level-Measurement for property information. DSLM adopts the variable characteristics of property information as the element of measurement. By applying this method, it is possible to provide information management functions to cope with the changes of each property information security level of an individual actively. It is expected that this will improve the security of previous information management methods even more and also contribute to the improvement of security in integrated systems such as the integrated ID management system and electronic wallet.

• The Journal of Society for e-Business Studies
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• v.16 no.2
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• pp.129-142
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• 2011

Traditionally research in Service Oriented Architecture(SOA) security has focused primarily on exploiting standards and solutions separately. There exists no unified methodology for SOA security to manage risks at the enterprise level. It needs to analyze preliminarily security threats and to manage enterprise risks by identifying vulnerabilities of SOA. In this paper, we propose a metric-based vulnerability assessment method using dynamic properties of services in SOA. The method is to assess vulnerability at the architecture level as well as the service level by measuring run-time dependency between services. The run-time dependency between services is an important characteristic to understand which services are affected by a vulnerable service. All services which directly or indirectly depend on the vulnerable service are exposed to the risk. Thus run-time dependency is a good indicator of vulnerability of SOA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1307-1323
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• 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 KIISE:Computing Practices and Letters
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• v.9 no.4
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• pp.470-485
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• 2003

The steganography is one of methods that users can hide data. Some steganography softwares use audio data among multimedia data. However, these commercialized audio steganography softwares have disadvantages that the existence of hidden messages can or easily recognized visually and only certain-sized data can be hidden. To solve these problems, this study suggested, designed and implemented Dynamic Message Embedding (DME) algorithm. Also, to improve the security level of the secret message, the file encryption algorithm has been applied. Through these, StegoWaveK system that performs audio steganography was designed and implemented. Then, the suggested system and the commercialized audio steganography system were compared and analyzed on criteria of the Human Visilable System (HVS), Human Auditory System (HAS), Statistical Analysis (SA), and Audio Measurement (AM).

• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.