All attacks in a computer network begin with an intruder's action of affecting the services provided to legitimate users. Hence, intrusion detection is vital for preserving integrity, confidentiality, and availability in a computer network. Intrusion detection faces many challenges, such as the need for large amount of data to discriminate between intruders and non-intruders, and the overlapping of user behavior to that of the intruders. This paper aims to target both of these challenges, by employing a distributed intrusion prevention system based on the Binary Partitle Swarm Optimization (BPSO) and Probabilistic Neural Network (PNN) algorithms. Such a system is capable of: 1) locally classifying actions as intruder or non-intruder type, and 2) consulting neighbors for casting a majority vote, upon finding high ambiguity on a decision. The algorithm uses an evolutionary computation approach to select the best features that can help classify intruders, while using fewer amounts of data. Furthermore, the approach uses concepts from semi-supervised learning to improve and adapt over time, to any network infrastructure. To demonstrate the viability of the proposed approach, a random set of data has been selected from the KDD-99 dataset. Such a set contained capture data from both users and attackers. Results have been compared with traditional data mining algorithms from previous work, demonstrating that such a system can have high accuracy, while maintaining a low false alarm rate.

Internet eXchange Points (IXPs) have been recently identified as critical components of the Internet's AS level infrastructure. Recent studies have characterized the importance of IXPs in Internet topology evolution studies but the network benefits brought about by the increased peering has not garnered much attention. In this work, we propose and implement a measurement framework to compare paths traversing an IXP hop with a set of valid alternate paths which isolate the IXP effects. Our framework enables identification of these alternate paths and then carries out an in-depth comparison of the IXP path with its available alternates. Using the popular route performance metric of end- to-end latency we observe that most paths through peering ASes at exchanges generally have a better alternate through transit providers of the peering ASes. Traditional overlay routes such as detour paths also outperform the default BGP path through an IXP. While economic benefits are the primary driving forces behind public peering we conclude that obtaining network benefits (in terms of path latencies) require due consideration by peering ASes to ensure better end-user experience.

The Internet ecosystem comprising of thousands of Autonomous Systems (ASes) now include Internet eXchange Points (IXPs) as another critical component in the infrastructure. With the growth of peering worldwide, IXPs are playing an increasing role not only in the topology evolution of the Internet but also inter-domain path routing. In this work, we first motivate the need to monitor popular exchange points based on geographical locations. These major IXPs are responsible for transmitting 43% of the total IXP traffic and hence play a significant role in determining the effects of peering at IXPs on path latencies. Using the most important route performance metric of end to end latency we then propose and implement a measurement framework to carry out an in-depth comparison of paths traversing an IXP with other available alternate paths. We observe the presence of a significant percentage of alternate paths outperforming the default route through an IXP in all major regions. Even though IXPs are set up primarily with economic benefits in mind, we conclude that there is a huge potential for improvement with more efficient routing and better planning at the primary IXPs around the world.

Particle Swarm Optimization (PSO) is a promising evolutionary algorithm, which has been used in a wide range of applications, due to its simple implementation, fast convergence, parallel behavior, and versatility in working with continuous and discrete domains. In this paper, we consider its application to the load balancing problem, in green smart homes. Specifically, an adapted version of the Binary PSO has been used to determine the optimal distribution of energy resources, across different green energy sources in a green smart home. The case study of interest considers the usage of solar and wind energy, as green energy sources for the green smart home. Results demonstrate the effectiveness of the algorithm, in terms of the optimal outcome (efficient distribution of energy resources), finding installation material surplus, and the execution speed of the algorithm.

For future wireless networks, a major challenge is how to efficiently support applications and services to end-users. Currently, most existing solutions attempt to address this issue on the application layer. In this paper, we take a different approach and we present a unified routing algorithm to efficiently enable applications in a Heterogeneous Wireless Mesh Network (HWMN). The algorithm builds upon service-oriented architecture (SOA) concepts, and more importantly, it provides confidential and reliable communication links over the mesh. Three techniques are implemented: key management, key shifting, and node relations. Such an approach provides a guideline to design secure and service-oriented HWMNs.

The Internet is a network of Autonomous Systems (ASes) comprising of a complex and complicated ecosystem of net- works used for a wide variety of applications. ASes exhibit varied functionality and communicate according to predefined rules to maintain distinct business objectives; termed intra- AS relations. These relations are one of two types: customer- provider (hierarchical) or peering (flat). Recent studies of intra-AS relations indicate the gradual transition of the In- ternet ecosystem from the hierarchical structure to a flatter peering architecture [1]. This infrastructure level flattening is characterized by the constant growth, rewiring and deaths of inter-AS links. Primary driving forces behind these changes are economic; especially the meteoric rise in popularity of organizations such as Facebook, Google, Yahoo and Microsoft, who have lately deployed large, private WAN infrastructures [1]. The transition from the hierarchical Internet has also accelerated with the deployment of multiple Internet eXchange Points (IXPs) worldwide, the facilitator of peering. Numerous peering links (between ASes) at these IXPs have recently been uncovered but their effects on Internet topology and inter- domain routing performance not yet examined. Exchange points (shown in fig 1) provide an infrastructure for ASes to set up mutually agreeable peering agreements at a common location and enable the quick exchange of traffic without requiring higher tier transit providers. They also facilitate dynamic changing of peering agreements be- tween Internet Service Providers (ISPs) providing transit to customer ASes. These customer ASes obtain better network performance (lesser delays, more reliability) while the ISPs save substantially on transit costs.

Much research exists for the efficient processing of spatio-temporal data streams. However, all methods ultimately rely on an ill-equipped processor [22], namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous spatio-temporal queries over spatio-temporal data streams. GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous spatio-temporal queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. © 2010 IEEE.

The recent uncovering of a high number of peering links at Internet eXchange Point (IXP) locations across the world has made these exchange switches a critical component of the Internet Autonomous System (AS) level ecosystem. Studies concentrating on the Internet topology evolution have surmised that numerous links hidden at these exchange points hold the key towards solving the missing links problem in studying the evolution of the AS-level topology of the Internet. In this work, we study the effect of this set of hitherto unseen peering links on the visible Internet topology. Starting from a simple visual analysis and continuing with a more advanced graph based metric analysis, we conclude that IXP links follow power law increase characteristics while exhibiting definitive clustering characteristics. Moreover, these additional links affect the joint degree distributions of nodes with higher degrees while leaving most other types of nodes unchanged. We conclude that the currently inferred AS-level maps of the Internet demonstrate considerable variations with the incorporation of these new links and could eventually provide us a better understanding of Internet topology evolution.

In order to test the proposed distributed intrusion detection system there is a need for sufficient hardware and software in place as to provide a reasonable approximation of the actual conditions that such a system would expect to encounter if deployed in a live environment. These hardware and software requirements are compounded when attempting to test a distributed grid computing system because such systems typically can often scale to the hundreds or even thousands of computers. Even if sufficient hardware is obtainable, the generation of accurate test data accurately depicting normal patterns of network or Internet traffic can provide additional challenges, especially when the data is time sensitive. This paper demonstrates a method by which a distributed grid based IDS can be designed and implemented using virtual servers deployed on Amazon.com's elastic compute cloud service.

Simulation and game content includes the levels, models, textures, items, and other objects encountered and possessed by players during the game. In most modern video games and in simulation software, the set of content shipped with the product is static and unchanging, or at best, randomized within a narrow set of parameters. However, ideally, if game content could be constantly and automatically renewed, players would remain engaged longer. This paper introduces two novel technologies that take steps toward achieving this ambition: 1) a new algorithm called content-generating NeuroEvolution of Augmenting Topologies (cgNEAT) is introduced that automatically generates graphical and game content while the game is played, based on the past preferences of the players, and 2) Galactic Arms Race (GAR), a multiplayer video game, is constructed to demonstrate automatic content generation in a real online gaming platform. In GAR,which is available to the public and playable online, players pilot space ships and fight enemies to acquire unique particle system weapons that are automatically evolved by the cgNEAT algorithm. A study of the behavior and results from over 1000 registered online players shows that cgNEAT indeed enables players to discover a wide variety of appealing content that is not only novel, but also based on and extended from previous content that they preferred in the past. Thus,GARis the first demonstration of evolutionary content generation in an online multiplayer game. The implication is that with cgNEAT it is now possible to create applications that generate their own content to satisfy users, potentially reducing the cost of content creation and increasing entertainment value from single-player to massively multiplayer online games (MMOGs) with a constant stream of evolving content.

Interactive Evolutionary Computation (IEC) creates the intriguing possibility that a large variety of useful content can be produced quickly and easily for practical computer graphics and gaming applications. To show that IEC can produce such content, this paper applies IEC to particle system effects, which are thedefactomethod in computer graphics for generating fire, smoke, explosions, electricity, water, and many other special effects. While particle systems are capable of producing a broad array of effects, they require substantial mathematical and programming knowledge to produce. Therefore, efficient particle system generation tools are required for content developers to produce special effects in a timely manner. This paper details the design, representation, and animation of particle systems via two IEC tools called NEAT Particles and NEAT Projectiles. Both tools evolve artificial neural networks (ANN) with the NeuroEvolution of Augmenting Topologies (NEAT) method to control the behavior of particles. NEAT Particles evolves general-purpose particle effects, whereas NEAT Projectiles specializes in evolving particle weapon effects for video games. The primary advantage of this NEAT-based IEC approach is to decouple the creation of new effects from mathematics and programming, enabling content developers without programming knowledge to produce complex effects. Furthermore, it allows content designers to produce a broader range of effects than typical development tools. Finally, it acts as a concept generator, allowing content creators to interactively and efficiently explore the space of possible effects. Both NEAT Particles and NEAT Projectiles demonstrate how IEC can evolve useful content for graphical media and games, and are together a step toward the larger goal of automated content generation.

Abstract Clusters, composed of symmetric multiprocessor (SMP) machines and heterogeneous machines, have become increasingly popular for high‐performance computing. Message‐passing libraries, such as message‐passing interface (MPI) and parallel virtual machine (PVM), arede factoparallel programming libraries for clusters that usually consist of homogeneous and uni‐processor machines. For SMP machines, MPI is combined with multithreading libraries like POSIX Thread and OpenMP to take advantage of the architecture. In addition to existing parallel programming libraries that are in C/C++ and FORTRAN programming languages, the Java programming language presents itself as another alternative with its object‐oriented framework, platform neutral byte code, and ever‐increasing performance. This paper presents a new parallel programming model and a library, VCluster, which implements this model. VCluster is based on migrating virtual threads instead of processes to support clusters of SMP machines more efficiently. The implementation uses thread migration, which can be used in dynamic load balancing. VCluster was developed in pure Java, utilizing the portability of Java to support clusters of heterogeneous machines. Several applications are developed to illustrate the use of this library and compare the usability and performance of VCluster with other approaches. Copyright © 2007 John Wiley & Sons, Ltd.

While recent measurement studies have shown the effectiveness of P2P network in media streaming, there have been questions raised about the quality of service (QoS), reliability of streaming services and sub optimal uplink utilization in particular. P2P streaming systems are inherently less reliable because of churn, internet dynamics, node heterogeneity and randomness in the swarm. We present a new model for P2P media streaming based on clustering of peers, called alliances. We show that alliance formation is a loosely coupled and an effective way to organize the peers. We show that our model maps to a ldquosmall-worldrdquo network, which form efficient overlay structures and are robust to network perturbations such as churn. We present a comparative simulation based study of our model with CoolStreaming/DONet and present a quantitative performance evaluation. Simulation results are promising and show that our model scales well under varying workloads and conditions, delivers near optimal levels of QoS, and for most cases, performs at par or even better than CoolStreaming/DONet.

In this paper, we present a swarm based peer to peer media streaming model. In our approach, nodes that serve the maximum content to their peers have a higher utility to the swarm and become candidates for direct relay of stream from the media server. This reward forms a natural incentive for the nodes to contribute to the swarm. Nodes cluster into small groups, called alliances, for a symbiotic association. Alliance members must exchange pieces to have the complete packet. This policy dissuades non-cooperating members in an alliance from their selfish behavior. We study our model using a simulator and present some initial results

In this paper, an evolutionary algorithm is presented to develop a solution for the variable radii sensor placement optimization problem. Sensor nodes are placed in a sensor field such that maximum coverage of the target region is achieved. Coverage is defined in terms of monitoring points of interest. In point coverage, it is desired to place the sensor nodes such that certain target points in the area to be monitored are efficiently covered. The sensors are assumed to have varying communication and sensing radii within specified ranges. To attain a certain degree of fault tolerance, the network is required to have k-connectivity. Therein, the challenge lies in finding a solution that provides the optimal choice and arrangement of sensor nodes with a minimized overall energy consumption. Our devised algorithm places the sensors to form an energy-efficient and fault tolerant network that provides coverage of the given query points

In most image processing and computer vision applications, real-world scale can only be determined when calibration information is available. Dynamic scenes further complicate most situations. However, some types of dynamic scenes provide useful information that can be used to recover real-world scale. In this paper, we focus on ocean scenes and propose a method for finding sizes in real-world units and the sea state from an uncalibrated camera. Fourier transforms in the space and time dimensions yield spatial and temporal frequency spectra. For water waves, the dispersion relation defines a square relationship between the wavelength and period of a wave. Our method applies this dispersion relation to recover the real-world scale of an ocean sequence. The sea state--including the peak wavelength and period, the wind speed that generated the waves, and the wave heights--is also determined from the frequency spectrum of the sequence combined with stochastic oceanography models. The process is demonstrated on synthetic and real sequences, validating the results with known scene geometry. This has wide applications in port monitoring and coastal surveillance.

Sensor network comprises of scattered sensor nodes with limited computational capabilities and battery power. The existing security solutions for traditional wireless networks can not be used because of the constraints associated with sensor network. We present secure sink node architecture as two-tiered scheme for sensor network security. The architecture protects the sink node from unauthorized access by surrounding it with two protection layers. Sink nodes listen to only inner layer nodes and inner nodes are allowed to communicate with only outer layer nodes. These protection layers are formed in an intelligent manner without violating constraints specific to sensor network. In order to enhance security, protection layers are re-adjusted in case of an attack. We present statistical analysis to elucidate the performance of proposed architecture.

Fair sharing of bandwidth remains an unresolved issue for distributed systems. In this paper, the users of a distributed LAN are modeled as selfish users with independence to choose their individual strategies. With these selfish users, the contention-based distributed medium access scenario is modeled as a complete-information, noncooperative game, designated the "access game". A novel MAC strategy based on p-persistent CSMA is presented to achieve fairness in the "access game". It is proven that there are an infinite number of nash equilibria for the "access game", but they do not result in fairness. Therefore, it may be beneficial for the selfish users to adhere to a set of constraints that result in fairness in a noncooperative fashion. This leads to the formulation of a constrained "access game" with fairness represented as a set of algebraic constraints. It is proven that the solution of the constrained game, the constrained nash equilibrium, is unique. Further, it is shown that, in addition to achieving fairness, this solution also optimizes the throughput. Finally, these results are extended to a more realistic incomplete-information scenario by approximating the incomplete-information scenario as a complete-information scenario through information gathering and dissemination.

We have analyzed the distributed MAC protocols by using game theoretic concepts. Specifically, we have modelled the "access" game as a non-cooperative game with incomplete information. We have proven the existence of optimal strategies for individual users. We have derived approximate formulae for optimal transmission probabilities in two cases: single class of users and two classes of users.

In this paper we apply a novel algorithm to control and optimize the number of non-critical calls in a video-intensive mobile environment supporting critical applications. The algorithm is based on graceful QoS reduction policy. We simulate a video-intensive scenario and determine the optimal bandwidth allocation required for maximizing the number of non-critical calls.

This correspondence develops a multiplier for two's complement numbers, similar to the quasi-serial multiplier which operates on sign magnitude numbers. The method offers an alternative to add shift techniques for low cost two's complement multiplication.