Worms have been improved and a range of sophisticated techniques have been integrated, which make the detection and response processes much harder and longer than in the past. Therefore, in this thesis, a STAKCERT (Starter Kit for Computer Emergency Response Team) model is built to detect worms attack in order to respond to worms more efficiently. The novelty and the strengths of the STAKCERT model lies in the method implemented which consists of STAKCERT KDD processes and the development of STAKCERT worm classification, STAKCERT relational model and STAKCERT worm apoptosis algorithm. The new concept introduced in this model which is named apoptosis, is borrowed from the human immunology system has been mapped in terms of a security perspective. Furthermore, the encouraging results achieved by this research are validated by applying the security metrics for assigning the weight and severity values to trigger the apoptosis. In order to optimise the performance result, the standard operating procedures (SOP) for worm incident response which involve static and dynamic analyses, the knowledge discovery techniques (KDD) in modeling the STAKCERT model and the data mining algorithms were used. This STAKCERT model has produced encouraging results and outperformed comparative existing work for worm detection. It produces an overall accuracy rate of 98.75% with 0.2% for false positive rate and 1.45% is false negative rate. Worm response has resulted in an accuracy rate of 98.08% which later can be used by other researchers as a comparison with their works in future.

Masters of Science / The aim of this study was to develop a Stock Control Assessment Tool for use in the public health care sector and then to apply it to identify problems in the stock control system. This would help authorities to optimize the system. The advantages experienced with such a dynamic Assessment Tool were many: The Tool was quick and easy to apply; it was user friendly; it provided an immediate SWOT analysis of a particular facility; it is in line with the SA NDP directives and it provides an indication of which structures are in place and whether they are functioning properly. Furthermore; it could determine the increase or decrease in performance of a facility (therefore identify trends within the functional status of a system) when data is collected over a period of time; and finally, it could also be used to prioritize drug policy directives. The following steps were established in the development of the Tool: A literature review of pharmaceutical stock control and Drug Supply Management was addressed to provide the background information for the motivation of this study and to identify the various elements that could influence stock control at a facility level. Observational studies were applied at selected private and public facilities to observe the impact of these identified stock control elements. An 'ideal' stock control system was then generated from the literature review and observational assessment. A structured questionnaire was developed and surveyed at these facilities to generate key areas of concern of a stock control system. A study and adaptation of the indicator methods used by the World Health Organization (WHO) to monitor drug use in health facilities resulted in the formulation of a practical Stock Control Assessment Tool based on 11 key indicators and a number of sub-indicators, all of which were objectively defined. The Tool was then applied at selected public facilities and the results were analyzed quantitatively, qualitatively and subjectively. Each of the indicators was then applied and results examined closely with a view to possible refinements of the indicator. The refinements were made and the Tool was re-applied at two selected facilities. These two facilities were randomly selected from the original six facilities included for the testing of the Tool. Final conclusions and specific recommendations were generated to improve the stock control systems at the selected public health care facilities.

Worms have been improved and a range of sophisticated techniques have been integrated, which make the detection and response processes much harder and longer than in the past. Therefore, in this thesis, a STAKCERT (Starter Kit for Computer Emergency Response Team) model is built to detect worms attack in order to respond to worms more efficiently. The novelty and the strengths of the STAKCERT model lies in the method implemented which consists of STAKCERT KDD processes and the development of STAKCERT worm classification, STAKCERT relational model and STAKCERT worm apoptosis algorithm. The new concept introduced in this model which is named apoptosis, is borrowed from the human immunology system has been mapped in terms of a security perspective. Furthermore, the encouraging results achieved by this research are validated by applying the security metrics for assigning the weight and severity values to trigger the apoptosis. In order to optimise the performance result, the standard operating procedures (SOP) for worm incident response which involve static and dynamic analyses, the knowledge discovery techniques (KDD) in modeling the STAKCERT model and the data mining algorithms were used. This STAKCERT model has produced encouraging results and outperformed comparative existing work for worm detection. It produces an overall accuracy rate of 98.75% with 0.2% for false positive rate and 1.45% is false negative rate. Worm response has resulted in an accuracy rate of 98.08% which later can be used by other researchers as a comparison with their works in future. / Ministry of Higher Education, Malaysia and Universiti Sains Islam Malaysia (USIM)