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Recent surveys indicate that the financial impact and operating losses due to insider intrusions are increasing. But these studies often disagree on what constitutes an " insider; " indeed, many define it only implicitly. In theory, appropriate selection of, and enforcement of, properly specified security policies should prevent legitimate users from(More)
Much of the literature on insider threat assumes, explicitly or implicitly, a binary, perimeter-based notion of an insider. However, it is generally accepted that this notion is unrealistic. The Attribute-Based Group Access Control (ABGAC) framework is a generalization of Role-Based Access Control (RBAC) which allows us to define a non-binary notion of "(More)
Parallel computation in a high performance computing environment can be characterized by the distributed memory access patterns of the underlying algorithm. During execution , networks of compute nodes exchange messages that indirectly exhibit these access patterns. Thus, identifying the algorithm underlying these observable messages is the problem of(More)
—The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on(More)
We introduce a theory of sequential causal inference in which learners in a chain estimate a structural model from their upstream "teacher" and then pass samples from the model to their downstream "student". It extends the population dynamics of genetic drift, recasting Kimura's selectively neutral theory as a special case of a generalized drift process(More)
Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network(More)
High Performance Computing (HPC) is a field concerned with solving large-scale problems in science and engineering. However, the computational infrastructure of HPC systems can also be misused as demonstrated by the recent commodi-tization of cloud computing resources on the black market. As a first step towards addressing this, we introduce a machine(More)
Cloud computing offers a scalable, low-cost, and resilient platform for critical applications. Securing these applications against attacks targeting unknown vulnerabilities is an unsolved challenge. Network anomaly detection addresses such zero-day attacks by modeling attributes of attack-free application traffic and raising alerts when new traffic deviates(More)