We propose a multiscale approach to modeling cyber networks, with the goal of capturing a view of the network and overall situational awareness with respect to a few key properties - connectivity, distance, and centrality - for a system under an active attack. We focus on theoretical and algorithmic foundations of multiscale graphs, coming from an algorithmic perspective, with the goal of modeling cyber system defense as a specific use case scenario. We first define a notion of multiscale graphs, in contrast with their well-studied single- scale counterparts. We develop multiscale analogs of paths and distance metrics. As a simple, motivating example of a common metric, we present a multiscale analog of the all-pairs shortest-path problem, along with a multiscale analog of a well-known algorithm which solves it. From a cyber defense perspective, this metric might be used to model the distance from an attacker's position in the network to a sensitive machine. In addition, we investigate probabilistic models of connectivity. These models exploit the hierarchy to quantify the likelihood that sensitive targets might be reachable from compromised nodes. We believe that our novel multiscale approach to modeling cyber-physical systems will advance several aspects of cyber defense, specifically allowing for a more efficient and agile approach to defending these systems.

Network-of-networks (NoN) is a graph-theoretic model of interdependent networks that have distinct dynamics at each network (layer). By adding special edges to represent relationships between nodes in different layers, NoN provides a unified mechanism to study interdependent systems intertwined in a complex relationship. While NoN based models have been proposed for cyber-physical systems, in this position paper we build towards a three-layered NoN model for an enterprise cyber system. Each layer captures a different facet of a cyber system. We present in-depth discussion for four major graph-theoretic applications to demonstrate how the three-layered NoN model can be leveraged for continuous system monitoring and mission assurance. A longer version of this paper can be accessed from arXiv [1].

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored. Imposing realtime constraints on a parallel (cluster) computing environment introduces a variety of challenges with respect to the formal verification of the system's timing properties. In this paper, we give a motivating example to demonstrate the need for such a system- an application to estimate the electromechanical states of the power grid- and we introduce an formal method for performing verification of certain temporal properties within a system of parallel processes. We describe our work towards a full real-time implementation of the target application- namely, our progress towards extracting a key mathematical kernel from the application, the formal process by which we analyze the intricate timing behavior of the processes on the cluster, as well as timing measurements taken on our test cluster to demonstrate use of these concepts.

The imposition of real-time constraints on a parallel computing environment- specifically high-performance, cluster-computing systems- introduces a variety of challenges with respect to the formal verification of the system's timing properties. In this paper, we briefly motivate the need for such a system, and we introduce an automaton-based method for performing such formal verification. We define the concept of a consistent parallel timing system: a hybrid system consisting of a set of timed automata (specifically, timed Buchi automata as well as a timed variant of standard finite automata), intended to model the timing properties of a well-behaved real-time parallel system. Finally, we give a brief case study to demonstrate the concepts in the paper: a parallel matrix multiplication kernel which operates within provable upper time bounds. We give the algorithm used, a corresponding consistent parallel timing system, and empirical results showing that the system operates under the specified timing constraints.

Geologic storage projects associated with large anthropogenic sources of greenhouse gases (GHG) will have lifecycles that may easily span a century, involve several numerical simulation cycles, and have distinct modeling teams. The process used for numerical simulation of the fate of GHG in the subsurface follows a generally consistent sequence of steps that often are replicated by scientists and engineers around the world. Site data is gathered, assembled, interpreted, and assimilated into conceptualizations of a solid-earth model; assumptions are made about the processes to be modeled; a computational domain is specified and spatially discretized; driving forces and initial conditions are defined; the conceptual models, computational domain, and driving forces are translated into input files; simulations are executed; and results are analyzed. Then, during and after the GHG injection, a continuous monitoring of the reservoir is done and models are updated with the newly collected data. Typically the working files generated during all these steps are maintained on workstations with local backups and archived once the project has concluded along with any modeling notes and records. We are proposing a new concept for supporting the management of full-scale GHG storage projects where collaboration, flexibility, accountability and long-term access will be essential features: The Geologic Sequestration Software Suite, GS3.

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored, largely due to the fact that until now, there has not been a need for such an environment. In this paper, we motivate the need for a cluster computing infrastructure capable of supporting computation over large datasets in real-time. Our motivating example is an analytical framework to support the next generation North American power grid, which is growing both in size and complexity. With streaming sensor data in the future power grid potentially reaching rates on the order of terabytes per day, the task of analyzing this data subject to real-time guarantees becomes a daunting task which will require the power of high-performance cluster computing capable of functioning under real-time constraints. One specific challenge that such an environment presents is the need for real-time networked communication between cluster nodes. In this paper, we discuss the need for real-time high-performance cluster computation, along with our work-in-progress towards an infrastructure which will ultimatelymore » enable such an environment.« less

Modern scientific enterprises are inherently knowledge-intensive. In general, scientific studies in domains such as geoscience, chemistry, physics and biology require the acquisition and manipulation of large amounts of experimental and field data in order to create inputs for large-scale computational simulations. The results of these simulations must then be analyzed, leading to refinements of inputs and models and further simulations. In this paper we describe our efforts in creating a knowledge management platform to support collaborative, wide-scale studies in the area of geologic sequestration modeling. The platform, known as GS3 (Geologic Sequestration Software Suite), exploits and integrates off-the-shelf software components including semantic wikis, content management systems and open source middleware to create the core architecture. We then extend the wiki environment to support the capture of provenance, the ability to incorporate various analysis tools, and the ability to launch simulations on supercomputers. The paper describes the key components of GS3 and demonstrates its use through illustrative examples. We conclude by assessing the suitability of our approach for geologic sequestration modeling and generalization to other scientific problem domains.

The weblog, or blog, has become a popular form of social media, through which authors can write posts, which can in turn generate feedback in the form of user comments. When considered in totality, a collection of blogs can thus be viewed as a sort of informal collection of mass sentiment and opinion. An obvious topic of interest might be to mine this collection to obtain some gauge of public sentiment over the wide variety of topics contained therein. However, the sheer size of the so-called blogosphere , combined with the fact that the subjects of posts can vary over a practically limitless number of topics poses some serious challenges when any meaningful analysis is attempted. Namely, the fact that largely anyone with access to the Internet can author their own blog, raises the serious issue of credibility---should some blogs be considered to be more influential than others, and consequently, when gauging sentiment with respect to a topic, should some blogs be weighted more heavily than others? In addition, as new posts and comments can be made on almost a constant basis, any blog analysis algorithm must be able to handle such updates efficiently. In this paper, we give a formalization of the blog model. We give formal methods of quantifying sentiment and influence with respect to a hierarchy of topics, with the specific aim of facilitating the computation of a per-topic, influence-weighted sentiment measure. Finally, as efficiency is a specific endgoal, we give upper bounds on the time required to update these values with new posts, showing that our analysis and algorithms are scalable.

Cyber security analysts in different geographical and organizational domains are often largely tasked with similar duties, albeit with domain-specific variations. These analysts necessarily perform much of the same work independently- for instance, analyzing the same list of security bulletins released by largely the same set of software vendors. As such, communication and collaboration between such analysts would be mutually beneficial to the analysts involved, potentially reducing redundancy and offering the opportunity to preemptively alert each other to high-severity security alerts in a more timely fashion. However, several barriers to practical and efficient collaboration exist, and consequently, no such framework exists to support these efforts. In this paper, we discuss the inherent difficulties which make efficient collaboration between cyber security analysts a difficult goal to achieve. We discuss preliminary ideas and concepts towards a collaborative cyber-security framework currently under development, whose goal is to facilitate analyst collaboration across these boundaries. While still in its early stages, we describe work-in-progress towards achieving this goal, including motivation, functionality, concepts, and a high-level description of the proposed system architecture.

Aspect-Oriented Programming (ADP) has been studied as a programming paradigm of increasing interest. Developed to address the problem of separation of concerns, a central concept behind modern aspect systems is the ability to specify cross-cutting functionality in one central location (i.e., the aspect), rather than scattering the requisite code across the entire system, thus avoiding the code pollution that would otherwise be necessary in implementing such functionality. The canonical example is that of logging, in which calls to any function (or subset thereof) are to be logged. Without aspects, the programmer would have no choice but to scatter the logging code throughout the entire system, which would be unwieldly (in the number of functions that would need to be modified). In an aspect-oriented language, the logging functionality would be encapsulated in its own entity, and a pointcut associated with the aspect defines when the code is to fire. In this way, functionality that otherwise cuts across an entire system is still easily encapsulated. While several production languages currently exist, our focus in this dissertation is on a formal model for aspect languages—specifically, semantics, types, and optimizations. To this end, in this dissertation, we study the theoretical foundations of a minimal aspect calculus.

We present a preliminary report on typing systems for polyadic μABC, aspect oriented programming---pointcuts and advice---and nothing else. Tuples of uninterpreted names are used to trigger advice. The resulting language is remarkably unstructured: the least common denominator of the pi-calculus and Linda. As such, developing meaningful type systems is a substantial challenge. Our work is guided by the translation of richly typed languages into μABC, specifically function- and class-based languages augmented with advice. The "impedance mismatch" between source and target is severe, and this leads us to a novel treatment of types in μABC.

Confluent graphs capture the connection properties of train tracks, offering a very natural generalization of planar graphs, and--as the example of railroad maps shows--are an important tool in graph visualization. In this paper we continue the study of confluent graphs, introducing strongly confluent graphs and tree-confluent graphs. We show that strongly confluent graphs can be recognized in NP (the complexity of recognizing confluent graphs remains open). We also give a natural elimination ordering characterization of tree-confluent graphs, and we show that this class coincides with the (6,2)-chordal bipartite graphs. Finally, we define outerconfluent graphs and identify the bipartite permutation graphs as a natural subclass.

Aspect-oriented programming (AOP) has been touted as a promising paradigm for managing complex softwaresecurity concerns. Roughly, AOP allows the security-sensitive events in a system to be specified separately from core functionality. The events of interest are specified in a pointcut. When a pointcut triggers, control is redirected to advice, which intercepts the event, potentially redirecting it to an error handler. Many interesting security properties are historydependent; however, currently deployed pointcut languages cannot express history-sensitivity (mechanisms like cflow in AspectJ capture only the current call stack.) We present a language of pointcuts with past-time temporal operators and discuss their implementation using a variant of security automata. The main result is a proof that the implementation is correct. Refining our earlier work ([6]), we define a minimal language of events and aspects in which “everything is an aspect”. The minimalist approach serves to clarify the issues and may be of independent interest.

Marcus Schaefer, Daniel Stefankovi c. Decidability of string graphs. J. Comput. System Sci. 68 (2004), no. 2, p. 319{334 (a preliminary version appeared in STOC 2001, p. 241{246.) Abstract: We show that string graphs can be recognized in NEXP by giving an exponential upper bound on the number of intersections for a drawing realizing the string graph in the plane. This upper bound con rms a conjecture by Kratochv l and Matou sek and settles the long-standing open problem of the decidability of string graph recognition. Finally we show how to apply the result to solve another old open problem: deciding the existence of Euler diagrams. The decidability of string graphs was independently proved by Pach and T oth (Graph Drawing 2001, Discrete Comput. Geom. 28 (2002), no. 4, 593{606.)

In the Paired Pointset Traversal problem we ask if, given two sets A = {a 1, ..., a n } and B = {b 1, ..., b n } in the plane, there is an ordering π of the points such that both a π(1), ..., a π(n) and b π(1), ..., b π(n) are self-avoiding polygonal arcs? We show that Paired Pointset Traversal is NP-complete. This has consequences for the complexity of computing the Fréchet distance of two-dimensional surfaces. We also show that the problem can be solved in polynomial time if the points in A and B are in convex position, and derive some combinatorial estimates on lct(A,B), the length of a longest common traversal of A and B.