Resilient Critical Infrastructure Management using Service Oriented Architecture

Abstract—The SERSCIS project aims to support the use of interconnected systems of services in Critical Infrastructure (CI) applications. The problem of system interconnectedness is aptly demonstrated by ‘Airport Collaborative Decision Making’ (ACDM). Failure or underperformance of any of the interlinked ICT systems may compromise the ability of airports to plan their use of resources to sustain high levels of air traffic, or to provide accurate aircraft movement forecasts to the wider European air traffic management systems. The proposed solution is to introduce further SERSCIS ICT components to manage dependability and interdependency. These use semantic models of the critical infrastructure, including its ICT services, to identify faults and potential risks and to increase human awareness of them. Semantics allows information and services to be described in such a way that makes them understandable to computers. Thus when a failure (or a threat of failure) is detected, SERSCIS components can take action to manage the consequences, including changing the interdependency relationships between services. In some cases, the components will be able to take action autonomously — e.g. to manage ‘local’ issues such as the allocation of CPU time to maintain service performance, or the selection of services where there are redundant sources available. In other cases the components will alert human operators so they can take action instead. The goal of this paper is to describe a Service Oriented Architecture (SOA) that can be used to address the management of ICT components and interdependencies in critical infrastructure systems. Index Terms—resilience; QoS; SOA; critical infrastructure, SLA

Selecting fast folding proteins by their rate of convergence

We propose a general method for predicting potentially good folders from a given number of amino acid sequences. Our approach is based on the calculation of the rate of convergence of each amino acid chain towards the native structure using only the very initial parts of the dynamical trajectories. It does not require any preliminary knowledge of the native state and can be applied to different kinds of models, including atomistic descriptions. We tested the method within both the lattice and off-lattice model frameworks and obtained several so far unknown good folders. The unbiased algorithm also allows to determine the optimal folding temperature and takes at least 3--4 orders of magnitude less time steps than those needed to compute folding times

Onset of word form recognition in English, Welsh, and English-Welsh bilingual infants

Children raised in the home as English or Welsh monolinguals or English–Welsh bilinguals were tested on untrained word form recognition using both behavioral and neurophysiological procedures. Behavioral measures confirmed the onset of a familiarity effect at 11 months in English but failed to identify it in monolingual Welsh infants between 9 and 12 months. In the neurophysiological procedure the familiarity effect was detected as early as 10 months in English but did not reach significance in monolingual Welsh. Bilingual children showed word form familiarity effects by 11 months in both languages and also revealed an online time course for word recognition that combined effects found for monolingual English and Welsh. To account for the findings, accentual, grammatical, and sociolinguistic differences between English and Welsh are considered

Multiple-Use Mechanisms for Attachment to Seat Tracks

A Seat Track Attach Mechanism (SAM) is a multiple-use clamping device intended for use in mounting various objects on the standard seat tracks used on the International Space Station (ISS). The basic SAM design could also be adapted to other settings in which seat tracks are available: for example, SAM-like devices could be used as universal aircraft-seat-track mounting clamps. A SAM (see figure) is easily installed by inserting it in a seat track, then actuating a locking lever to clamp the SAM to the track. The SAM includes an over-center locking feature that prevents premature disengagement that could be caused by some inadvertent movements of persons or objects in the vicinity. A SAM can be installed in, or removed from, any position along a seat track, without regard for the locations of the circular access holes. Hence, one or more SAM(s) can be used to mount an object or objects on a track or a pair of tracks in an infinite number of preferred configurations. A SAM can be incorporated into a dual swivel device, so that two of the SAMs can be made to lock onto two side-by-side seat tracks simultaneously, as would be the case in a standard ISS rack bay where two side-by-side racks reside. The main benefit to using two SAMs in a side-by-side arrangement is to provide a coupled load. By picking up load points on two seat tracks, a coupled loading is created, improving the stability and strength since the load is spread to two seat tracks at a short distance

Optimising the analysis of transcript data using high density oligonucleotide arrays and genomic DNA-based probe selection

Background: Affymetrix GeneChip arrays are widely used for transcriptomic studies in a diverse range of species. Each gene is represented on a GeneChip array by a probe-set, consisting of up to 16 probe-pairs. Signal intensities across probe-pairs within a probe-set vary in part due to different physical hybridisation characteristics of individual probes with their target labelled transcripts. We have previously developed a technique to study the transcriptomes of heterologous species based on hybridising genomic DNA (gDNA) to a GeneChip array designed for a different species, and subsequently using only those probes with good homology. Results: Here we have investigated the effects of hybridising homologous species gDNA to study the transcriptomes of species for which the arrays have been designed. Genomic DNA from Arabidopsis thaliana and rice (Oryza sativa) were hybridised to the Affymetrix Arabidopsis ATH1 and Rice Genome GeneChip arrays respectively. Probe selection based on gDNA hybridisation intensity increased the number of genes identified as significantly differentially expressed in two published studies of Arabidopsis development, and optimised the analysis of technical replicates obtained from pooled samples of RNA from rice. Conclusion: This mixed physical and bioinformatics approach can be used to optimise estimates of gene expression when using GeneChip arrays

A Signal-Processing View on Packet Sampling and Anomaly Detection

International audienceAnomaly detection methods typically operate on preprocessed traffic traces. Firstly, most traffic capturing devices today employ random packet sampling, where each packet is selected with a certain probability, to cope with increasing link speeds. Secondly, temporal aggregation, where all packets in a measurement interval are represented by their temporal mean, is applied to transform the traffic trace to the observation timescale of interest for anomaly detection. These preprocessing steps affect the temporal correlation structure of traffic that is used by anomaly detection methods such as Kalman filtering or PCA, and have thus an impact on anomaly detection performance. Prior work has analyzed how packet sampling degrades the accuracy of anomaly detection methods; however, neither theoretical explanations nor solutions to the sampling problem have been provided. This paper makes the following key contributions: (i) It provides a thorough analysis and quantification of how random packet sampling and temporal aggregation modify the signal properties by introducing noise, distortion and aliasing. (ii) We show that aliasing introduced by the aggregation step has the largest impact on the correlation structure. (iii) We further propose to replace the aggregation step with a specifically designed low-pass filter that reduces the aliasing effect. (iv) Finally, we show that with our solution applied, the performance of anomaly detection systems can be considerably improved in the presence of packet sampling

Applying PCA for Traffic Anomaly Detection: Problems and Solutions

International audienceSpatial Principal Component Analysis (PCA) has been proposed for network-wide anomaly detection. A recent work has shown that PCA is very sensitive to calibration settings. Unfortunately, the authors did not provide further explanations for this observation. In this paper, we fill this gap and provide the reasoning behind the found discrepancies. We revisit PCA for anomaly detection and evaluate its performance on our data. We develop a slightly modified version of PCA that uses only data from a single router. Instead of correlating data across different spatial measurement points, we correlate the data across different metrics. With the help of the analyzed data, we explain the pitfalls of PCA and underline our argumentation with measurement results. We show that the main problem is that PCA fails to capture temporal correlation. We propose a solution to deal with this problem by replacing PCA with the Karhunen-Loeve transform. We find that when we consider temporal correlation, anomaly detection results are significantly improved