Vikten av säkra nätverk

The purpose of this thesis is to show how to use vulnerability testing to identify and search for security flaws in networks of computers. The goal is partly to give a casual description of different types of methods of vulnerability testing and partly to present the method and results from a vulnerability test. A document containing the results of the vulnerability test will be handed over and a solution to the found high risk vulnerabilities. The goal is also to carry out and present this work as a form of a scholarly work.The problem was to show how to perform vulnerability tests and identify vulnerabilities in the organization's network and systems. Programs would be run under controlled circumstances in a way that they did not burden the network. Vulnerability tests were conducted sequentially, when data from the survey was needed to continue the scan.A survey of the network was done and data in the form of operating system, among other things, were collected in the tables. A number of systems were selected from the tables and were scanned with Nessus. The result was a table across the network and a table of found vulnerabilities. The table of vulnerabilities has helped the organization to prevent these vulnerabilities by updating the affected computers. Also a wireless network with WEP encryption, which is insecure, has been detected and decrypted.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.