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Teaching computer science students proper design, implementation, and maintenance of secure systems is a challenging task that calls for the use of novel pedagogical tools. Because of today's multitude of vulnerable systems and security threats, it is vital that computer science students be taught techniques for programming secure systems, especially since many of them will work on systems with sensitive data after graduation. With today's prevalence of Internet-connected systems storing sensitive data and the omnipresent threat of technically skilled malicious users, computer security remains a critically important field. Finally, we defined an interface for further communication with existing visualization tools, which depict the program execution using visualizations specific to the policy model. Furthermore, an Access Control Shell was designed as an interactive command interface to execute the wrapper APIs, as well as a test platform or a container to launch student program. From another hand, students can monitor how the process is affected by the policy through this tool and adjust the rules accordingly. The program and policy exist at the user level. The Programming Library provides a system call wrapper API which enforces the developed policy in the execution of a process. A student can write a policy and then run programs under the policy. In this report, we propose an access control programming library which can provide students hand-on experience with the effect of an access control policy on a running program. Though they can benefit the learning through analyzing or visualizing access control policies, few of them are designed to teach development of access control policies. Education access control tools have been promoted. The high complexity of advanced security models in the modern trusted systems requires an effective formal education for students. A user study of DTEvisual suggests that the tool is helpful for students to understand DTE. With DTEvisual, students have an environment for visualizing a DTE-based policy using graphs, visually modifying the policy, and animating the common DTE queries in real time. Domain Type Enforcement (DTE) is a powerful abstraction for teaching students about modern models of access control in operating systems. Since streamlines are space curves, the proposed method also serves as a general curve segmentation method and may be applied in other fields such as computer vision.īesides flow visualization, a pedagogical visualization tool DTEvisual for teaching access control is also discussed in this dissertation. It is shown that the proposed method can locate interesting features (e.g., a spiral in a streamline) more accurately than some other flow feature extraction methods. This dissertation proposes a machine learning-based streamline segmentation algorithm to segment each streamline into distinct features.
For instance, many flow feature extraction techniques segment streamline based on simple heuristics such as accumulative curvature or arc length, and, as a result, the segments they found usually do not directly correspond to complete flow features. This problem has not been studied extensively in flow visualization. To this end, this dissertation proposes to segment each streamline into different features. For example, it is useful to find all the spirals contained in different streamlines and present them to users. In addition to focusing on similar streamlines through streamline similarity query or clustering, users sometimes want to group and see similar features from different streamlines. Compared with a recent streamline similarity measure, the proposed measure allows users to see the interesting features more clearly in a complicated vector field.
The proposed measure is tested in two common tasks in vector field exploration: streamline similarity query and streamline clustering. Different from other streamline similarity measures, the proposed one considers both the distribution of and the distances among features along a streamline. This dissertation presents a novel streamline similarity measure inspired by the bag-of-features concept from computer vision. Measuring the similarity between two streamlines is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying and streamline clustering.