A Hybrid Approach For Information Systems Security Risk Assessment In Electronic Business

In electronic business environment, it is critical for an enterprise to assess information systems security (ISS) risks. In this paper, we propose a hybrid approach for ISS risk assessment in e-business. Given there is a great deal of uncertainty in the ISS risk assessment in e-business environment, in the hybrid approach, we combine the evidence theory with fuzzy sets to deal with the uncertain evidence found in the ISS risk assessment. The proposed approach provides a new way to define the basic belief assignment in fuzzy measure. Moreover, the approach also provides a method of testing the evidential consistency, which can reduce the uncertainty derived from the conflicts of evidence. Finally, the approach is further demonstrated and validated via a case study, in which sensitivity analysis is employed to validate the reliability of the proposed approach

A methodology for validation of integrated systems models with an application to coastal-zone management in South-West Sulawesi

Integrated Systems Models (ISMs) have been developed over decades to support the planning and management of natural resources and the environment. The development of these models is based on the concepts of systems approach and integrated approach. However, the lack of a generally accepted definition of model validity and model validation, the inherent complexity of ISMs, the poor predictive value of historical data related to the natural-human system, the scarcity of field data and the high level of aggregation of ISMs make the validation of ISMs an extremely difficult task (Chapter 1). These problems raise a number of important questions, such as: to what extent can such models contribute to our knowledge and ability to manage the environment? Do they have added value in comparison with conventional process models? Centred in these questions are the two questions: how can the validity of an ISM be defined? How can this validity be determined? This thesis is aimed at answering these two questions

Task-Driven Integrity Assessment and Control for Vehicular Hybrid Localization Systems

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems