Biblio

We propose a modification to the framework of Universally Composable (UC) security [3]. Our new notion involves comparing the real protocol execution with an ideal execution involving ideal functionalities (just as in UC-security), but allowing the environment and adversary access to some super-polynomial computational power. We argue the meaningfulness of the new notion, which in particular subsumes many of the traditional notions of security. We generalize the Universal Composition theorem of [3] to the new setting. Then under new computational assumptions, we realize secure multi-party computation (for static adversaries) without a common reference string or any other set-up assumptions, in the new framework. This is known to be impossible under the UC framework.

Tensors are a multi-linear generalization of matrices to their d-way counterparts, and are receiving intense interest recently due to their natural representation of high-dimensional data and the availability of fast tensor decomposition algorithms. Given the input-output data of a nonlinear system/circuit, this paper presents a nonlinear model identification and simulation framework built on top of Volterra series and its seamless integration with tensor arithmetic. By exploiting partially-symmetric polyadic decompositions of sparse Toeplitz tensors, the proposed framework permits a pleasantly scalable way to incorporate high-order Volterra kernels. Such an approach largely eludes the curse of dimensionality and allows computationally fast modeling and simulation beyond weakly nonlinear systems. The black-box nature of the model also hides structural information of the system/circuit and encapsulates it in terms of compact tensors. Numerical examples are given to verify the efficacy, efficiency and generality of this tensor-based modeling and simulation framework.

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.

The emerging software-defined networking (SDN) technology decouples the control plane from the data plane in a computer network with open and standardized interfaces, and hence opens up the network designers’ options and ability to innovate. The wide adoption of SDN in industry has motivated the development of large-scale, high-fidelity testbeds for evaluation of systems that incorporate SDN. In this article, we develop a framework to support OpenFlow-based SDN simulation and distributed emulation, by leveraging our prior work on a hybrid network testbed with a parallel network simulator and a virtual-machine-based emulation system. We show how to exploit typical SDN controller behaviors to handle performance issues caused by the centralized controller in parallel discrete-event simulation. In particular, we develop an asynchronous synchronization algorithm for passive SDN controllers and design a two-level architecture for active SDN controllers. We evaluate the system performance, showing good scalability. Finally, we present a case study, using the testbed, to evaluate network verification applications in an SDN-based data center network. CCS Concepts: Networks→Network simulations; Computing methodologies→Simulation

Workflows capture complex operational processes and include security constraints limiting which users can perform which tasks. An improper security policy may prevent cer- tain tasks being assigned and may force a policy violation. Deciding whether a valid user-task assignment exists for a given policy is known to be extremely complex, especially when considering user unavailability (known as the resiliency problem). Therefore tools are required that allow automatic evaluation of workflow resiliency. Modelling well defined workflows is fairly straightforward, however user availabil- ity can be modelled in multiple ways for the same workflow. Correct choice of model is a complex yet necessary concern as it has a major impact on the calculated resiliency. We de- scribe a number of user availability models and their encod- ing in the model checker PRISM, used to evaluate resiliency. We also show how model choice can affect resiliency computation in terms of its value, memory and CPU time.

Fat-tree topologies have been widely adopted as the communication network in data centers in the past decade. Nowa- days, high-performance computing (HPC) system designers are considering using fat-tree as the interconnection network for the next generation supercomputers. For extreme-scale computing systems like the data centers and supercomput- ers, the performance is highly dependent on the intercon- nection networks. In this paper, we present FatTreeSim, a PDES-based toolkit consisting of a highly scalable fat-tree network model, with the goal of better understanding the de- sign constraints of fat-tree networking architectures in data centers and HPC systems, as well as evaluating the applica- tions running on top of the network. FatTreeSim is designed to model and simulate large-scale fat-tree networks up to millions of nodes with protocol-level fidelity. We have con- ducted extensive experiments to validate and demonstrate the accuracy, scalability and usability of FatTreeSim. On Argonne Leadership Computing Facility’s Blue Gene/Q sys- tem, Mira, FatTreeSim is capable of achieving a peak event rate of 305 M/s for a 524,288-node fat-tree model with a total of 567 billion committed events. The strong scaling experiments use up to 32,768 cores and show a near linear scalability. Comparing with a small-scale physical system in Emulab, FatTreeSim can accurately model the latency in the same fat-tree network with less than 10% error rate for most cases. Finally, we demonstrate FatTreeSim’s usability through a case study in which FatTreeSim serves as the net- work module of the YARNsim system, and the error rates for all test cases are less than 13.7%.

Realistic and scalable testing systems are critical to evaluate network applications and protocols to ensure successful real system deployments. Container-based network emula- tion is attractive because of the combination of many desired features of network simulators and physical testbeds . The success of Mininet, a popular software- defined networking (SDN) emulation testbed, demonstrates the value of such approach that we can execute unmodified binary code on a large- scale emulated network with lightweight OS-level vir- tualization techniques. However, an ordinary network em- ulator uses the system clock across all the containers even if a container is not being scheduled to run. This leads to the issue of temporal fidelity, especially with high workloads. Virtual time sheds the light on the issue of preserving tem- poral fidelity for large-scale emulation. The key insight is to trade time with system resources via precisely scaling the time of interactions between containers and physical devices by a factor of n, hence, making an emulated network ap- pear to be n times faster from the viewpoints of applications in the container. In this paper, we develop a lightweight Linux-container-based virtual time system and integrate the system to Mininet for fidelity and scalability enhancement. We also design an adaptive time dilation scheduling mod- ule for balancing speed and accuracy. Experimental results demonstrate that (1) with virtual time, Mininet is able to accurately emulate a network n times larger in scale, where n is the scaling factor, with the system behaviors closely match data obtained from a physical testbed; and (2) with the adaptive time dilation scheduling, we reduce the running time by 46% with little accuracy loss. Finally, we present a case study using the virtual-time-enabled Mininet to evalu- ate the limitations of equal-cost multi-path (ECMP) routing in a data center network.

In this paper, we propose an accumulated loss recovery algorithm on overlay multicast system using Fountain codes. Fountain code successfully decodes the packet loss, but it is weak in accumulated losses on multicast tree. The proposed algorithm overcomes an accumulated loss and significantly reduces delay on overlay multicast tree.
 

Electrical Distribution Networks face new challenges by the Smart Grid deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.
 

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.

Electrical Distribution Networks face new challenges by the Smart Grid deployment. The required metering infrastructures add new vulnerabilities that need to be taken into account in order to achieve Smart Grid functionalities without considerable reliability trade-off. In this paper, a qualitative assessment of the cyber attack impact on the Advanced Metering Infrastructure (AMI) is initially attempted. Attack simulations have been conducted on a realistic Grid topology. The simulated network consisted of Smart Meters, routers and utility servers. Finally, the impact of Denial-of-Service and Distributed Denial-of-Service (DoS/DDoS) attacks on distribution system reliability is discussed through a qualitative analysis of reliability indices.

A key question that arises in rigorous analysis of cyberphysical systems under attack involves establishing whether or not the attacked system deviates significantly from the ideal allowed behavior. This is the problem of deciding whether or not the ideal system is an abstraction of the attacked system. A quantitative variation of this question can capture how much the attacked system deviates from the ideal. Thus, algorithms for deciding abstraction relations can help measure the effect of attacks on cyberphysical systems and to develop attack detection strategies. In this paper, we present a decision procedure for proving that one nonlinear dynamical system is a quantitative abstraction of another. Directly computing the reach sets of these nonlinear systems are undecidable in general and reach set over-approximations do not give a direct way for proving abstraction. Our procedure uses (possibly inaccurate) numerical simulations and a model annotation to compute tight approximations of the observable behaviors of the system and then uses these approximations to decide on abstraction. We show that the procedure is sound and that it is guaranteed to terminate under reasonable robustness assumptions.

The Maude-NRL Protocol Analyzer (Maude-NPA) is a tool for reasoning about the security of cryptographic protocols in which the cryptosystems satisfy different equational properties. It tries to find secrecy or authentication attacks by searching backwards from an insecure attack state pattern that may contain logical variables, in such a way that logical variables become properly instantiated in order to find an initial state. The execution mechanism for this logical reachability is narrowing modulo an equational theory. Although Maude-NPA also possesses a forwards semantics naturally derivable from the backwards semantics, it is not suitable for state space exploration or protocol simulation. In this paper we define an executable forwards semantics for Maude-NPA, instead of its usual backwards one, and restrict it to the case of concrete states, that is, to terms without logical variables. This case corresponds to standard rewriting modulo an equational theory. We prove soundness and completeness of the backwards narrowing-based semantics with respect to the rewriting-based forwards semantics. We show its effectiveness as an analysis method that complements the backwards analysis with new prototyping, simulation, and explicit-state model checking features by providing some experimental results.