Browsing by Author "Chen, Wan-Jia"

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A hybrid information security risk assessment procedure considering interdependences between controls
(2012-01-01)
Lo, Chi-Chun
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Chen, Wan-Jia
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Risk assessment is the core process of information security risk management. Organizations use risk assessment to determine the risks within an information system and provide sufficient means to reduce these risks. In this paper, a hybrid procedure for evaluating risk levels of information security under various security controls is proposed. First, this procedure applies the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach to construct interrelations among security control areas. Secondly, likelihood ratings are obtained through the Analytic Network Process (ANP) method: as a result, the proposed procedure can detect the interdependences and feedback between security control families and function in real world situations. Lastly, the Fuzzy Linguistic Quantifiers-guided Maximum Entropy Order-Weighted averaging (FLQ-MEOWA) operator is used to aggregate impact values assessed by experts, applied to diminish the influence of extreme evaluations such as personal views and drastic perspectives. A real world application in a branch office of the health insurance institute in Taiwan was examined to verify the proposed procedure. By analyzing the acquired data, we confirm the proposed procedure certainly detects the influential factors among security control areas. This procedure also evaluates risk levels more accurately by coping with the interdependencies among security control families and determines the information systems safeguards required for better security, therefore enabling organizations to accomplish their missions. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
A Real-time Crime Detection System Based on Lawful Interception - A Case Study of MSN Messenger
(2011)
Chen, Chi-Hua
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Chen, Wan-Jia
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Wang, Ya-Fen
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
In recent years, the number of online crimes (e. g., the various emerging scams and criminal schemes) has increased. Online crime suspects utilize the anonymous nature of web to disguise their identity through various methods and evade detection and surveillance from law enforcement agencies. This study proposes an effective lawful interception system, Real-time Crime Detection System (RCDS), which includes Criminal Investigation Bureau (CIB), Internet Service Providers (ISP), and Network Interception Server (NIS) to establish a legally sanctioned lawful interception process which can collect and store evidence for criminal detection in real time. We provide a case study of MSN Messenger to describe the MSN Protocol (MSNP), information flow process, and lawful interception methodologies. The RCDS can provide criminal detection and surveillance services for MSN to obtain criminal communication records in turn reducing online criminal activities. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]
A Real-time Traffic Information Model Using GPS-based Probe Car Data
(2011)
Chen, Wan-Jia
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Chen, Chi-Hua
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Lin, Bon-Yeh
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.