197,829 research outputs found
DIVERSE: a Software Toolkit to Integrate Distributed Simulations with Heterogeneous Virtual Environments
We present DIVERSE (Device Independent Virtual Environments- Reconfigurable, Scalable, Extensible), which is a modular collection of complimentary software packages that we have developed to facilitate the creation of distributed operator-in-the-loop simulations. In DIVERSE we introduce a novel implementation of remote shared memory (distributed shared memory) that uses Internet Protocol (IP) networks. We also introduce a new method that automatically extends hardware drivers (not in the operating system kernel driver sense) into inter-process and Internet hardware services. Using DIVERSE, a program can display in a CAVEâ„¢, ImmersaDeskâ„¢, head mounted display (HMD), desktop or laptop without modification. We have developed a method of configuring user programs at run-time by loading dynamic shared objects (DSOs), in contrast to the more common practice of creating interpreted configuration languages. We find that by loading DSOs the development time, complexity and size of DIVERSE and DIVERSE user applications is significantly reduced. Configurations to support different I/O devices, device emulators, visual displays, and any component of a user application including interaction techniques, can be changed at run-time by loading different sets of DIVERSE DSOs. In addition, interpreted run-time configuration parsers have been implemented using DIVERSE DSOs; new ones can be created as needed.
DIVERSE is free software, licensed under the terms of the GNU General Public License (GPL) and the GNU Lesser General Public License (LGPL) licenses.
We describe the DIVERSE architecture and demonstrate how DIVERSE was used in the development of a specific application, an operator-in-the-loop Navy ship-board crane simulator, which runs unmodified on a desktop computer and/or in a CAVE with motion base motion queuing
A consensus based network intrusion detection system
Network intrusion detection is the process of identifying malicious behaviors
that target a network and its resources. Current systems implementing intrusion
detection processes observe traffic at several data collecting points in the
network but analysis is often centralized or partly centralized. These systems
are not scalable and suffer from the single point of failure, i.e. attackers
only need to target the central node to compromise the whole system. This paper
proposes an anomaly-based fully distributed network intrusion detection system
where analysis is run at each data collecting point using a naive Bayes
classifier. Probability values computed by each classifier are shared among
nodes using an iterative average consensus protocol. The final analysis is
performed redundantly and in parallel at the level of each data collecting
point, thus avoiding the single point of failure issue. We run simulations
focusing on DDoS attacks with several network configurations, comparing the
accuracy of our fully distributed system with a hierarchical one. We also
analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND
SECURITY 2015 IN KUALA LUMPUR, MALAYSI
D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization
We propose a distributed algorithm, named Distributed Alternating Direction
Method of Multipliers (D-ADMM), for solving separable optimization problems in
networks of interconnected nodes or agents. In a separable optimization problem
there is a private cost function and a private constraint set at each node. The
goal is to minimize the sum of all the cost functions, constraining the
solution to be in the intersection of all the constraint sets. D-ADMM is proven
to converge when the network is bipartite or when all the functions are
strongly convex, although in practice, convergence is observed even when these
conditions are not met. We use D-ADMM to solve the following problems from
signal processing and control: average consensus, compressed sensing, and
support vector machines. Our simulations show that D-ADMM requires less
communications than state-of-the-art algorithms to achieve a given accuracy
level. Algorithms with low communication requirements are important, for
example, in sensor networks, where sensors are typically battery-operated and
communicating is the most energy consuming operation.Comment: To appear in IEEE Transactions on Signal Processin
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