Quantifying security risk is an important and yet difficult task in enterprise network security management. While metrics exist for individual software vulnerabilities, there is currently no standard way of aggregating such metrics. We present a model that can be used to aggregate vulnerability metrics in an enterprise network, producing quantitative metrics that measure the likelihood breaches can occur within a given network configuration. A clear semantic model for this aggregation is an important first step toward a comprehensive network security metric model. We utilize existing work in attack graphs and apply probabilistic reasoning to produce an aggregation that has clear semantics and sound computation. We ensure that shared dependencies between attack paths have a proportional effect on the final calculation. We correctly reason over cycles, ensuring that privileges are evaluated without any self-referencing effect. We introduce additional modeling artifacts in our probabilistic graphical model to capture and account for hidden correlations among exploit steps. The paper shows that a clear semantic model for aggregation is critical in interpreting the results, calibrating the metric model, and explaining insights gained from empirical evaluation. Our approach has been rigorously evaluated using a number of network models, as well as data from production systems.

Abstract.Various tools exist to analyze enterprise network systems and to produce attack graphs detailing how attackers might penetrate into the system. These attack graphs, however, are often complex and difficult to comprehend fully, and a human user may find it problematic to reach appropriate configuration decisions. This paper presents methodologies that can 1) automatically identify portions of an attack graph that do not help a user to understand the core security problems and so can be trimmed, and 2) automatically group similar attack steps as virtual nodes in a model of the network topology, to immediately increase the understandability of the data. We believe both methods are important steps toward improving visualization of attack graphs to make them more useful in configuration management for large enterprise networks. We implemented our methods using one of the existing attack-graph toolkits. Initial experimentation shows that the proposed approaches can 1) significantly reduce the complexity of attack graphs by trimming a large portion of the graph that is not needed for a user to understand the security problem, and 2) significantly increase the accessibility and understandability of the data presented in the attack graph by clearly showing, within a generated visualization of the network topology, the number and type of potential attacks to which each host is exposed.

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Raymond S. Pettit teaches courses in programming, artificial intelligence, objected oriented design, algorithms, theory of computation, and related subjects in ACU's School of Information Technology and Computing. Prior to joining the ACU faculty, he spent twenty years in software development, research, and training the Air Force Research Lab and NASA's Langley Research Center as well as private industry. His current research focuses on how automated assessment tools interact with student learning in university programming courses. Are Automated Assessment Tools Helpful in ProgrammingCourses? AbstractAutomated assessment tools (AATs) are growing in popularity in introductory programming courses, but researchers may have a difficult time synthesizing valid data to draw conclusions about the tools' usefulness. Our first step addressing this issue was to break down our overriding question-are automated assessment tools helpful in programming courses?-into four more specific questions: (1) Have AATs proven to be helpful in improving student learning? (2) Do students think that AATs have improved their performance? (3) After having used the tools, do instructors think that the tools have improved their teaching experiences? and (4) Is the assessment performed by AATs accurate enough to be helpful? In discussing the many AATs that exist, many researchers have only reported results relevant to one or two of these specific questions. We address each of our four questions separately and draw on data from 24 different tools to arrive at our conclusions. We determine that the literature demonstrates AATs helpfulness in student learning, instructor support, and assessment accuracy. However, we found results about students' opinions regarding the helpfulness of AATs to be inconclusive. Given our findings, we make suggestions both for instructors using these tools and to researchers creating them.

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As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.

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Abstract-Enterprise network security management is a complex task of balancing security and usability, with trade-offs often necessary between the two. Past work has provided ways to identify intricate attack paths due to misconfiguration and vulnerabilities in an enterprise system, but little has been done to address how to correct the security problems within the context of various other requirements such as usability, ease of access, and cost of countermeasures. This paper presents an approach based on Boolean Satisfiability Solving (SAT Solving) that can reason about attacks, usability requirements, cost of actions, etc. in a unified, logical framework. Preliminary results show that the approach is both effective and efficient.