Time correlated anomaly detection based on inferences

Anomaly detection techniques are used to find the presence of anomalous activities in a network by comparing traffic data activities against a "normal" baseline. Although it has several advantages which include detection of "zero-day" attacks, the question surrounding absolute definition of systems deviations from its "normal" behaviour is important to reduce the number of false positives in the system. This study proposes a novel multi-agent network-based framework known as Statistical model for Correlation and Detection (SCoDe), an anomaly detection framework that looks for timecorrelated anomalies by leveraging statistical properties of a large network, monitoring the rate of events occurrence based on their intensity. SCoDe is an instantaneous learning-based anomaly detector, practically shifting away from the conventional technique of having a training phase prior to detection. It does acquire its training using the improved extension of Exponential Weighted Moving Average (EWMA) which is proposed in this study. SCoDe does not require any previous knowledge of the network traffic, or network administrators chosen reference window as normal but effectively builds upon the statistical properties from different attributes of the network traffic, to correlate undesirable deviations in order to identify abnormal patterns. The approach is generic as it can be easily modified to fit particular types of problems, with a predefined attribute, and it is highly robust because of the proposed statistical approach. The proposed framework was targeted to detect attacks that increase the number of activities on the network server, examples which include Distributed Denial of Service (DDoS) and, flood and flash-crowd events. This paper provides a mathematical foundation for SCoDe, describing the specific implementation and testing of the approach based on a network log file generated from the cyber range simulation experiment of the industrial partner of this project.

Life history influences how fire affects genetic diversity in two lizard species

'Fire mosaics' are often maintained in landscapes to promote successional diversity in vegetation with little understanding of how this will affect ecological processes in animal populations such as dispersal, social organization and re-establishment. To investigate these processes, we conducted a replicated, spatiotemporal landscape genetics study of two Australian woodland lizard species [Amphibolurus norrisi (Agamidae) and Ctenotus atlas (Scincidae)]. Agamids have a more complex social and territory structure than skinks, so fire might have a greater impact on their population structure and thus genetic diversity. Genetic diversity increased with time since fire in C. atlas and decreased with time since fire in A. norrisi. For C. atlas, this might reflect its increasing population size after fire, but we could not detect increased gene flow that would reduce the loss of genetic diversity through genetic drift. Using landscape resistance analyses, we found no evidence that postfire habitat succession or topography affected gene flow in either species and we were unable to distinguish between survival and immigration as modes of postfire re-establishment. In A. norrisi, we detected female-biased dispersal, likely reflecting its territorial social structure and polygynous mating system. The increased genetic diversity in A. norrisi in recently burnt habitat might reflect a temporary disruption of its territoriality and increased male dispersal, a hypothesis that was supported with a simulation experiment. Our results suggest that the effects of disturbance on genetic diversity will be stronger for species with territorial social organization.

Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living : a pilot study

Since 1998, tele-rehabilitation has been extensively studied for its potential capacity of saving time and cost for both therapists and patients. However, one gap hindering the deployment of tele-rehabilitation service is the approach to evaluate the outcome after tele-rehabilitation exercises without the presence of professional clinicians. In this paper, we propose an approach to model jerky and jerky-free movement trajectories with hidden Markov models (HMMs). The HMMs are then utilised to identify the jerky characteristics in a motion trajectory, thereby providing the number and amplitude of jerky movements in the specific length of the trajectory. Eventually, the ability of performing functional upper extremity tasks can be evaluated by classifying the motion trajectory into one of the pre-defined ability levels by looking at the number and amplitude of jerky movements. The simulation experiment confirmed that the proposed method is able to correctly classify motion trajectories into various ability levels to a high degree.

Dispersal responses override density effects on genetic diversity during post-disturbance succession.

Dispersal fundamentally influences spatial population dynamics but little is known about dispersal variation in landscapes where spatial heterogeneity is generated predominantly by disturbance and succession. We tested the hypothesis that habitat succession following fire inhibits dispersal, leading to declines over time in genetic diversity in the early successional geckoNephrurus stellatus We combined a landscape genetics field study with a spatially explicit simulation experiment to determine whether successional patterns in genetic diversity were driven by habitat-mediated dispersal or demographic effects (declines in population density leading to genetic drift). Initial increases in genetic structure following fire were likely driven by direct mortality and rapid population expansion. Subsequent habitat succession increased resistance to gene flow and decreased dispersal and genetic diversity inN. stellatus Simulated changes in population density alone did not reproduce these results. Habitat-mediated reductions in dispersal, combined with changes in population density, were essential to drive the field-observed patterns. Our study provides a framework for combining demographic, movement and genetic data with simulations to discover the relative influence of demography and dispersal on patterns of landscape genetic structure. Our results suggest that succession can inhibit connectivity among individuals, opening new avenues for understanding how disturbance regimes influence spatial population dynamics.

Deep drawing behaviour of ultrafine grained copper : modelling and experiment

Ultrafine grained materials produced by severe plastic deformation methods possess attractive mechanical properties such as high strength compared with traditional coarse grained counterparts and reasonable ductility. Between existing severe plastic deformation methods the Equal Channel Angular Pressing is the most promising for future industrial applications and can produce a variety of ultrafine grained microstructures in materials depending on route, temperature and number of passes during processing. Driven by a rising trend of miniaturisation of parts these materials are promising candidates for microforming processes. Considering that bi-axial deformation of sheet (foil) is the major operation in microforming, the investigation of the influence of the number of ECAP passes on the bi-axial ductility in micro deep drawing test has been examined by experiments and FE simulation in this study. The experiments have showed that high force was required for drawing of the samples processed by ECAP compare to coarse grained materials. The limit drawing ratio of ultrafine grained samples was in the range of 1.9–2.0 with ECAP pass number changing from 1 to 16, while a higher value of 2.2 was obtained for coarse grained copper. However, the notable decrease in tensile ductility with increase in strength was not as pronounced for bi-axial ductility. The FE simulation using standard isotropic hardening model and von Mises yielding criterion confirmed these findings.

Computational simulation of the influence of inert particles on incomplete combustion of methane at a low air factor

It is well known that the gas–solid system plays a significant role in many industrial processes. It is a complex physical and chemical process, generally consisting of heat transfer, mass transfer, species diffusion, and chemical reactions. In this paper, the reaction of methane with air at a low air factor and the gas flow in a fluidized bed with 0.1 mm solid particles are computationally simulated to enable the study of the effect of the inert particles on the species diffusion and the chemical reactions. The reaction of methane and air is modeled by a two-step reaction mechanism that produces a continuous fluid phase composed of six gases (CH₄, CO, O₂, CO₂, H₂O, and N₂) and discrete solid particles in the reactor. The simulation results are compared with experiment and show that the finite rate model and the eddy dissipation model can well describe the reactions of gases in high-density gas–solid systems. The distribution of each gas and the particle behaviors are analyzed for incomplete combustion at different concentrations of loaded solid particles. The inert particles change the reactions by enhancing both the chemical kinetics and the species diffusion dynamics.

Formulation and simulation of a 3D mechanical model of embryos for microinjection

The understanding of cell manipulation, for example in microinjection, requires an accurate model of the cells. Motivated by this important requirement, a 3D particlebased mechanical model is derived for simulating the deformation of the fish egg membrane and the corresponding cellular forces during microrobotic cell injection. The model is formulated based on the kinematic and dynamic of spring- damper configuration with multi-particle joints considering the visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during microinjection. A preliminary simulation study is conducted with different parameter configurations. The results indicate that the proposed particle-based model is able to provide similar deformation profiles as observed from a real microinjection experiment of the zebrafish embryo published in the literature. As a generic modelling approach is adopted, the proposed model also has the potential in applications with different types of manipulation such as micropipette cell aspiration.

Pyrolysis of municipal green waste: a modelling, simulation and experimental analysis

Pyrolysis is the thermo-chemical conversion of carbonaceous feedstock in the absence of oxygen to produce bio-fuel (bio-oil, bio-char and syn-gas). Bio-fuel production from municipal green waste (MGW) through the pyrolysis process has attracted considerable attention recently in the renewable energy sector because it can reduce greenhouse gas emissions and contribute to energy security. This study analyses properties of MGW feedstock available in Rockhampton city of Central Queensland, Australia, and presents an experimental investigation of producing bio-fuel from that MGW through the pyrolysis process using a short sealed rotary furnace. It was found from the experiment that about 19.97% bio-oil, 40.83% bio-char and 29.77% syn-gas can be produced from the MGW. Then, a four-stage steady state simulation model is developed for pyrolysis process performance simulation using Aspen Plus software. In the first stage, the moisture content of the MGW feed is reduced. In the second stage, the MGW is decomposed according to its elemental constituents. In the third stage, condensate material is separated and, finally, the pyrolysis reactions are modelled using the Gibbs free energy minimisation approach. The MGW's ultimate and proximate analysis data were used in the Aspen Plus simulation as input parameters. The model is validated with experimentally measured data. A good agreement between simulation and experimental results was found. More specifically, the variation of modelling and experimental elemental compositions of the MGW was found to be 7.3% for carbon, 15.82% for hydrogen, 7.04% for nitrogen and 5.56% for sulphur. The validated model is used to optimise the biofuel production from the MGW as a function of operating variables such as temperature, moisture content, particle size and process heat air-fuel ratio. The modelling and optimisation results are presented, analysed and discussed.