Fire plays an important role in forest ecosystem management depending on the dual character of it. It should be managed and supervised effectively. In this particular study, the study area was located in the north part of Gävleborg County in Sweden, which is in a high- latitude region. Seven factors, divided into natural factors and human caused factors, were extracted from digital elevation model (DEM), classified land use map and feature shape files provided by National Land Survey of Sweden (Lantmäteriet). Two different weighting schemes for the factors were determined by the Analytic Hierarchy Process (AHP) method. With the help of ArcGIS 9.3 and Erdas 9.3, two classified result maps were obtained, where forest fire risk ranks were shown as five classes, very low, low, moderate, high and very high. The 43 fire incidents in the year 2007 and 2008 recorded by Global Fire Management System were used to evaluate the results. The results show that the higher rank the region is, the larger is the probability for forest fire risk and higher the risk to spread the fire. Furthermore, according to the occurrence time of the fire incidents, the period of time from end of May to beginning of June was generalized as a dangerous period for forest fire risk in this study area. After analyzing and discussing, even if there might be some uncertainties caused by variable selection, resolution problem and weighting schemes, the results were generally reliable.

Development of Cybersecurity processes and strategies should take two main approaches. One is to develop an efficient and effective set of methodologies to identify software vulnerabilities and patch them before being exploited. Second is to develop a set of methodologies to predict the behavior of attackers and execute defending techniques based on attacking behavior. Managing of Vulnerabilities and analyzing them is directly related to the first approach. Developing of methodologies and models to predict the behavior of attackers is related to the second approach. Both these approaches are inseparably interconnected. Our effort in this study mainly focuses on developing useful statistical models that can give us signals about the behavior of cyber attackers. Analytically understanding of vulnerabilities in statistical point of view helps to develop a set of statistical models that works as a bridge between Cybersecurity and Abstract Statistical and Mathematical knowledge. Any such effort should begin with properly understanding the nature of Vulnerabilities in a computer network system. We start this study with analyzing "Vulnerability" based on inferences that can be taken from National Vulnerability Database (NVD). In Cybersecurity context, we apply Markov approach to develop suitable predictive models to successfully estimate the minimum number of steps to compromise a security goal that an attacker would take using the concept of Expected Path Length (EPL). We have further developed Non-Homogeneous Stochastic model by improving EPL estimates in to a time dependent variable. This approach analytically applied in a simple model of computer network with discovered vulnerabilities resulted in several useful observations exemplifying the applicability in real world computer systems. The methodology indicated a measure of the "Risk" associated with the model network as a function of time indicating defending professionals on the threats they are facing and should anticipate to face. Furthermore, using a similar approach taken in well-known Google page rank algorithm, a new ranking algorithm of vulnerability ranks with respect to time for computer network system is also presented in this study. With better IT resources analytical models and methodologies presented in this study can be developed into more generalized versions and apply in real world computer network environments.