A defense system against DDoS attacks by large-scale IP traceback

In this paper, we present a new approach, called Flexible Deterministic Packet Marking (FDPM), to perform a large-scale IP traceback to defend against Distributed Denial of Service (DDoS) attacks. In a DDoS attack the victim host or network is usually attacked by a large number of spoofed IP packets coming from multiple sources. IP traceback is the ability to trace the IP packets to their sources without relying on the source address field of the IP header. FDPM provides many flexible features to trace the IP packets and can obtain better tracing capability than current IP traceback mechanisms, such as Probabilistic Packet Marking (PPM), and Deterministic Packet Marking (DPM). The flexibilities of FDPM are in two ways, one is that it can adjust the length of marking field according to the network protocols deployed; the other is that it can adjust the marking rate according to the load of participating routers. The implementation and evaluation demonstrates that the FDPM needs moderately only a small number of packets to complete the traceback process; and can successfully perform a large-scale IP traceback, for example, trace up to 110,000 sources in a single incident response. It has a built-in overload prevention mechanism, therefore this scheme can perform a good traceback process even it is heavily loaded.

Low-order estimation scheme for large-scale systems with interconnection delays

This paper deals with the practical aspects of reduced-order distributed functional state observers design for interconnected linear systems subject to time delays in the interconnections. Contrary to some estimation strategies which only take the ideal instantaneous output information into account, the proposed scheme incorporates output information that is inevitably encountered with time delays in the course of its transmission from the distanced subsystems. It is proved that such estimator possesses less restrictive existence conditions with the acceptance of measurement data from other interrelated subsystems. Upon the satisfaction of the established existence conditions, it will be demonstrated through a simple design procedure and simulation results that a feasible observer can be realized for a given numerical system.

New results on H∞ filtering for nonlinear large-scale systems with interconnected time-varying delays

This paper proposes a new design method of H∞ filtering for nonlinear large-scale systems with interconnected time-varying delays. The interaction terms with interval time-varying delays are bounded by nonlinear bounding functions including all states of the subsystems. A stable linear filter is designed to ensure that the filtering error system is exponentially stable with a prescribed convergence rate. By constructing a set of improved Lyapunov functions and using generalized Jensen inequality, new delay-dependent conditions for designing H∞ filter are obtained in terms of linear matrix inequalities. Finally, an example is provided to illustrate the effectiveness of the proposed result.

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the '. curse of dimensionality', is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively.

An LMI-based functional estimation scheme of large-scale time-delay systems with strong interconnections

In this paper, the problem of distributed functional state observer design for a class of large-scale interconnected systems in the presence of heterogeneous time-varying delays in the interconnections and the local state vectors is considered. The resulting observer scheme is suitable for strongly coupled subsystems with multiple time-varying delays, and is shown to give better results for systems with very strong interconnections while only some mild existence conditions are imposed. A set of existence conditions are derived along with a computationally simple observer constructive procedure. Based on the Lyapunov-Krasovskii functional method (LKF) in the framework of linear matrix inequalities (LMIs), delay-dependent conditions are derived to obtain the observer parameters ensuring the exponential convergence of the observer error dynamics. The effectiveness of the obtained results is illustrated and tested through a numerical example of a three-area interconnected system.

A parallel Euler approach for large-scale biological sequence assembly

Biological sequence assembly is an essential step for sequencing the genomes of organisms. Sequence assembly is very computing intensive especially for the large-scale sequence assembly. Parallel computing is an effective way to reduce the computing time and support the assembly for large amount of biological fragments. Euler sequence assembly algorithm is an innovative algorithm proposed recently. The advantage of this algorithm is that its computing complexity is polynomial and it provides a better solution to the notorious “repeat” problem. This paper introduces the parallelization of the Euler sequence assembly algorithm. All the Genome fragments generated by whole genome shotgun (WGS) will be assembled as a whole rather than dividing them into groups which may incurs errors due to the inaccurate group partition. The implemented system can be run on supercomputers, network of workstations or even network of PC computers. The experimental results have demonstrated the performance of our system.

An efficient replicated data access approach for large-scale distributed systems

In data-intensive distributed systems, replication is the most widely used approach to offer high data availability, low bandwidth consumption, increased fault-tolerance and improved scalability of the overall system. Replication-based systems implement replica control protocols that enforce a specified semantics of accessing the data. Also, the performance depends on a number of factors, the chief of which is the protocol used to maintain consistency among object replica. In this paper, we propose a new low-cost and high data availability protocol called the box-shaped grid structure for maintaining consistency of replicated data on networked distributed computing systems. We show that the proposed protocol provides high data availability, low communication costs, and increased fault-tolerance as compared to the baseline replica control protocols.

Analytical network modeling of heterogeneous large-scale cluster systems

The study of the communication networks for distributed systems is very important, since the overall performance of these systems is often depends on the effectiveness of its communication network. In this paper, we address the problem of networks modeling for heterogeneous large-scale cluster systems. We consider the large-scale cluster systems as a typical cluster of clusters system. Since the heterogeneity is becoming common in such systems, we take into account network as well as cluster size heterogeneity to propose the model. To this end, we present an analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Large-scale electrospinning of polymer nanofibers using needleless nozzle

In this study, we have demonstrated that a rotating metal wire coil can be used as a nozzle to electrospin nanofibers on a large-scale. Without using any needles, the rotating wire coil, partially immersed in a polymer solution reservoir, can pick up a thin layer of charged polymer solution and generate a large number of nanofibers from the wire surface simultaneously. This arrangement significantly increases the nanofiber productivity. The fiber productivity was found to be determined by the coil dimensions, applied voltage and polymer concentration. The dependency of fiber diameter on the polymer concentration showed a similar trend to that for a conventional electrospinning system using a syringe needle nozzle, but the coil electrospun fibers were thinner with narrower diameter distribution. The profiles of electric field strength in the coil electrospinning was calculated and showed concentrated electric field intensity on the wire surface.

CAFS : a novel lightweight cache-based scheme for large-scale intrusion alert fusion

In this paper, we present some practical experiences on implementing an alert fusion mechanism from our project. After investigation on most of the existing alert fusion systems, we found the current body of work alternatively weighed down in the mire of insecure design or rarely deployed because of their complexity. As confirmed by our experimental analysis, unsuitable mechanisms could easily be submerged by an abundance of useless alerts. Even with the use of methods that achieve a high fusion rate and low false positives, attack is also possible. To find the solution, we carried out analysis on a series of alerts generated by well-known datasets as well as realistic alerts from the Australian Honey-Pot. One important finding is that one alert has more than an 85% chance of being fused in the following five alerts. Of particular importance is our design of a novel lightweight Cache-based Alert Fusion Scheme, called CAFS. CAFS has the capacity to not only reduce the quantity of useless alerts generated by intrusion detection system, but also enhance the accuracy of alerts, therefore greatly reducing the cost of fusion processing. We also present reasonable and practical specifications for the target-oriented fusion policy that provides a quality guarantee on alert fusion, and as a result seamlessly satisfies the process of successive correlation. Our experiments compared CAFS with traditional centralized fusion. The results showed that the CAFS easily attained the desired level of simple, counter-escapable alert fusion design. Furthermore, as a lightweight scheme, CAFS can easily be deployed and excel in a large amount of alert fusions, which go towards improving the usability of system resources. To the best of our knowledge, our work is a practical exploration in addressing problems from the academic point of view. Copyright © 2011 John Wiley & Sons, Ltd.

Fine-scale effects of habitat loss and fragmentation despite large-scale gene flow for some regionally declining woodland bird species

Habitat loss and associated fragmentation effects are well-recognised threats to biodiversity. Loss of functional connectivity (mobility, gene flow and demographic continuity) could result in population decline in altered habitat, because smaller, isolated populations are more vulnerable to extinction. We tested whether substantial habitat reduction plus fragmentation is associated with reduced gene flow in three 'decliner' woodland-dependent bird species (eastern yellow robin, weebill and spotted pardalote) identified in earlier work to have declined disproportionately in heavily fragmented landscapes in the Box-Ironbark forest region in north-central Victoria, Australia. For these three decliners, and one 'tolerant' species (striated pardalote), we compared patterns of genetic diversity, relatedness, effective population size, sex-ratios and genic (allele frequency) differentiation among landscapes of different total tree cover, identified population subdivision at the regional scale, and explored fine-scale genotypic (individual-based genetic signature) structure. Unexpectedly high genetic connectivity across the study region was detected for 'decliner' and 'tolerant' species. Power analysis simulations suggest that moderate reductions in gene flow should have been detectable. However, there was evidence of local negative effects of reduced habitat extent and structural connectivity: slightly lower effective population sizes, lower genetic diversity, higher within-site relatedness and altered sex-ratios (for weebill and eastern yellow robin) in 10 x 10 km 'landscapes' with low vegetation cover. We conclude that reduced structural connectivity in the Box-Ironbark ecosystem may still allow sufficient gene flow to avoid the harmful effects of inbreeding in our study species. Although there may still be negative consequences of fragmentation for demographic connectivity, the high genetic connectivity of mobile bird species in this system suggests that reconnecting isolated habitat patches may be less important than increasing habitat extent and/or quality if these need to be traded off.

Disrupted fine-scale population processes in fragmented landscapes despite large-scale genetic connectivity for a widespread and common cooperative breeder : the superb fairy-wren (Malurus cyaneus)

Understanding how habitat fragmentation affects population processes (e.g. dispersal) at different spatial scales is of critical importance to conservation. We assessed the effects of habitat fragmentation on dispersal and regional and fine-scale population structure in a currently widespread and common cooperatively breeding bird species found across south-eastern Australia, the superb fairy-wren Malurus cyaneus. Despite its relative abundance and classification as an urban tolerant species, the superb fairy-wren has declined disproportionately from low tree-cover agricultural landscapes across the Box-Ironbark region of north-central Victoria, Australia. Loss of genetic connectivity and disruption to its complex social system may be associated with the decline of this species from apparently suitable habitat in landscapes with low levels of tree cover. To assess whether reduced structural connectivity has had negative consequences for genetic connectivity in the superb fairy-wren, we used a landscape-scale approach to compare patterns of genetic diversity and gene flow at large (landscape/regional) and fine (site-level) spatial scales. In addition, using genetic distances, for each sex, we tested landscape models of decreased dispersal through treeless areas (isolation-by-resistance) while controlling for the effect of isolation-by-distance. Landscape models indicated that larger-scale gene flow across the Box-Ironbark region was constrained by distance rather than by lack of structural connectivity. Nonetheless, a pattern of isolation-by-resistance for males (the less-dispersive sex) and lower genetic diversity and higher genetic similarity within sites in low-cover fragmented landscapes indicated disruption to fine-scale gene flow mechanisms and/or mating systems. Although loss of structural connectivity did not appear to impede gene flow at larger spatial scales, fragmentation appeared to affect fine-scale population processes (e.g. local gene flow mechanisms and/or mating systems) adversely and may contribute to the decline of superb fairy-wrens in fragmented landscapes in the Box-Ironbark region. © 2012 British Ecological Society.

Evaluation of salmon farming effects on marine systems in the inner seas of southern Chile : a large-scale mensurative experiment

The impact of salmon farming on the environment has been widely studied; there is, however, no consensus on the magnitude and quality of these effects and little information on their extent over large salmon farming areas. Forty-three salmon farm sites of which 29 are in full operation and grouped in nine locations in southern Chile were evaluated. Using statistical methods (two-way anova), no effects were found on water column variables such as nitrate, ammonia, orthophosphate and chlorophyll, whereas they were significant on sediment variables such as nitrogen (N), phosphorus (P) and organic carbon (OC), among others. Natural variability evaluation of some parameters revealed that P in sediments had the lowest coefficient of variation (23.2%) when compared with N (65.2%), particulate organic matter (POM) (139%) and OC (39%), and showed more marked salmon farming effects with no locality effects. Four of the nine locations showed stronger effects on sediments, with average P values ranging from 150 to 230 mmol that were six to nine times higher than in control sites (25 mmol). Nitrogen and carbon in sediments, while showing significant effects on salmon farming, also showed locality effects that revealed other biogenic processes influencing sediment composition. Thus, P in sediments was proposed as a promising indicator of impact on salmon farming, although the relationship with fauna in sediments was not linear and somewhat variable; more research is therefore needed to understand such connections. Considering entire geographical locations, no relationship was found between sediment conditions under salmon cages and the condition of the water column at a farm. This may indicate the possibility of high dilution rates and recycling processes, which so far preclude the detection of more global impacts beyond the cages shadow.

Understanding the sources of uncertainty to reduce the risks of undesirable outcomes in large-scale freshwater ecosystem restoration projects: an example from the Murray–Darling Basin, Australia

There are a growing number of large-scale freshwater ecological restoration projects worldwide. Assessments of the benefits and costs of restoration often exclude an analysis of uncertainty in the modelled outcomes. To address this shortcoming we explicitly model the uncertainties associated with measures of ecosystem health in the estuary of the Murray– Darling Basin, Australia and how those measures may change with the implementation of a Basin-wide Plan to recover water to improve ecosystem health. Specifically, we compare two metrics – one simple and one more complex – to manage end-of-system flow requirements for one ecosystem asset in the Basin, the internationally important Coorong saline wetlands. Our risk assessment confirms that the ecological conditions in the Coorong are likely to improve with implementation of the Basin Plan; however, there are risks of a Type III error (where the correct answer is found for the wrong question) associated with using the simple metric for adaptive management.