Abstract: The field of security metric and security evaluation is multifaceted and multidimensional in nature, which needs great care and systematic approach to evaluate. The security evaluation is a continuous process that should be carried out throughout the different software development stages and also in the operational phases. In practice the secure software development is based upon the guidelines and rules for secure design and coding. Even if the secure software development process and guidelines are to be followed, the resultant level of security remains unknown to the development team. A security evaluation framework that can be applied at the early system development stages,the derived metrics that act as indicators of security level of the system and point out the most critical component of the system , in order to provide the basis for the system developers to take the design decisions regarding security is the foremost requirement of secure software development. In this study we h

• Publication Date: 28-Apr-2013
• DOI: 10.15224/978-981-07-6260-5-17
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Abstract: Most real databases contain data whose correctness is uncertain. In order to work with such data, there is a need to quantify the integrity of the data. This is achieved by using probabilistic databases. Data uncertain is common in real world applications. The uncertainty can be controlled very prudently. In this paper, we are using probabilistic models on uncertain data and develop a novel method to calculate conditional probabilities for uncertain numerical attributes. Based on that, we propose a Naive Bayesian classifier algorithm for uncertain data(NBCU) using exponential distribution. The ultimate aim is determine the uncertainty of multiple attributes using our proposed approach (NBCU).The experimental results show that the proposed method classifies uncertain data with potentially higher accuracy.

• Publication Date: 28-Apr-2013
• DOI: 10.15224/978-981-07-6260-5-18
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