Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

The short reign of Sultan Osman and other stories

A character discovering and testing the limits of his emotional or psychological range most interests me. What will he choose to do? Stay within his old boundaries? Or try and go beyond them? What does he learn about himself in the process? And, finally, what price will be exacted, either for his staying where he is, or for his choosing a new level of self-knowledge? "The Short Reign Of Sultan Osman and Other Stories" is a collection of short stories set in either the United States, Greece, or Brazil, and ranging in time from 1972 to today. Each story presents its protagonist with challenges unique to a specific time and place. In most of these stories, the protagonists are driven by an urge for love or for mastery, and these urges send them across landscapes of delusion or folly before they can arrive at some sense of self-knowledge.

Combined effects of short-term exposure to elevated CO2 and decreased O2 on the physiology and energy budget of the thick shell mussel Mytilus coruscus

Hypoxia and ocean acidification are two consequences of anthropogenic activities. These global trends occur on top of natural variability. In environments such as estuarine areas, short-term acute pH and O2 fluctuations are occurring simultaneously. The present study tested the combined effects of short-term seawater acidification and hypoxia on the physiology and energy budget of the thick shell mussel Mytilus coruscus. Mussels were exposed for 72 h to six combined treatments with three pH levels (8.1, 7.7 and 7.3) and two dissolved oxygen (DO) levels (2 mg/L, 6 mg/L). Clearance rate (CR), food absorption efficiency (AE), respiration rate (RR), ammonium excretion rate (ER), O:N ratio and scope for growth (SFG) were significantly reduced, and faecal organic dry weight ratio (E) was significantly increased at low DO. Low pH did not lead to a reduced SFG. Interactive effects of pH and DO were observed for CR, E and RR. Principal component analysis (PCA) revealed positive relationships among most physiological indicators, especially between SFG and CR under normal DO conditions. These results demonstrate that Mytilus coruscus was sensitive to short-term (72 h) exposure to decreased O2 especially if combined with decreased pH levels. In conclusion, the short-term oxygen and pH variation significantly induced physiological changes of mussels with some interactive effects.

Ostracode relative abundances and geochemistry in surface sediments from Lake Ohrid

Understanding the ecology of bioindicators such as ostracods is essential in order to reconstruct past environmental and climate change from analysis of fossil assemblages preserved in lake sediment cores. Knowledge of the ecology of ancient Lake Ohrid's ostracod fauna is very limited and open to debate. In advance of the Ohrid ICDP-Drilling project, which has potential to generate high-resolution long-term paleoenvironmental data of global importance in paleoclimate research, we sampled Lake Ohrid and a wide range of habitat types in its surroundings to assess 1) the composition of ostracod assemblages in lakes, springs, streams, and short-lived seasonal water bodies, 2) the geographical distribution of ostracods, and 3) the ecological characteristics of individual ostracod species. In total, 40 species were collected alive, and seven species were preserved as valves and empty carapaces. Of the 40 ostracod species, twelve were endemic to Lake Ohrid. The most common genus in the lake was Candona, represented by 13 living species, followed by Paralimnocythere, represented by five living species. The most frequent species was Cypria obliqua. Species with distinct distributions included Heterocypris incongruens, Candonopsis kingsleii, and Cypria lacustris. The most common species in shallow, flooded areas was H. incongruens, and the most prominent species in ditches was C. kingsleii. C. lacustris was widely distributed in channels, springs, lakes, and rivers. Statistical analyses were performed on a "Lake Ohrid" dataset, comprising the subset of samples from Lake Ohrid alone, and an "entire" dataset comprising all samples collected. The unweighted pair group mean average (UPGMA) clustering was mainly controlled by species-specific depth preferences. Canonical Correspondence Analysis (CCA) with forward selection identified water depth, water temperature, and pH as variables that best explained the ostracod distribution in Lake Ohrid. The lack of significance of conductivity and dissolved oxygen in CCA of Ohrid data highlight the uniformity across the lake of the well-mixed waters. In the entire area, CCA revealed that ostracod distribution was best explained by water depth, salinity, conductivity, pH, and dissolved oxygen. Salinity was probably selected by CCA due to the presence of Eucypris virens and Bradleystrandesia reticulata in short-lived seasonal water bodies. Water depth is an important, although indirect, influence on ostracod species distribution which is probably associated with other factors such as sediment texture and food supply. Some species appeared to be indicators for multiple environmental variables, such as lake level and water temperature.

Short-term metabolic and growth responses of the cold-water coral lophelia pertusa to ocean acidification

Cold-water corals are amongst the most three-dimensionally complex deep-sea habitats known and are associated with high local biodiversity. Despite their importance as ecosystem engineers, little is known about how these organisms will respond to projected ocean acidification. Since preindustrial times, average ocean pH has already decreased from 8.2 to ~ 8.1. Predicted CO2 emissions will decrease this by up to another 0.3 pH units by the end of the century. This decrease in pH may have a wide range of impacts upon marine life, and in particular upon calcifiers such as cold-water corals. Lophelia pertusa is the most widespread cold-water coral (CWC) species, frequently found in the North Atlantic. Data here relate to a short term data set (21 days) on metabolism and net calcification rates of freshly collected L. pertusa from Mingulay Reef Complex, Scotland. These data from freshly collected L. pertusa from the Mingulay Reef Complex will help define the impact of ocean acidification upon the growth, physiology and structural integrity of this key reef framework forming species.

Effect of enhanced pCO2 levels on the production of dissolved organic carbon and transparent exopolymer particles in short-term bioassay experiments

It has been proposed that increasing levels of pCO2 in the surface ocean will lead to more partitioning of the organic carbon fixed by marine primary production into the dissolved rather than the particulate fraction. This process may result in enhanced accumulation of dissolved organic carbon (DOC) in the surface ocean and/or concurrent accumulation of transparent exopolymer particles (TEPs), with important implications for the functioning of the marine carbon cycle. We investigated this in shipboard bioassay experiments that considered the effect of four different pCO2 scenarios (ambient, 550, 750 and 1000 µatm) on unamended natural phytoplankton communities from a range of locations in the northwest European shelf seas. The environmental settings, in terms of nutrient availability, phytoplankton community structure and growth conditions, varied considerably between locations. We did not observe any strong or consistent effect of pCO2 on DOC production. There was a significant but highly variable effect of pCO2 on the production of TEPs. In three of the five experiments, variation of TEP production between pCO2 treatments was caused by the effect of pCO2 on phytoplankton growth rather than a direct effect on TEP production. In one of the five experiments, there was evidence of enhanced TEP production at high pCO2 (twice as much production over the 96 h incubation period in the 750 ?atm treatment compared with the ambient treatment) independent of indirect effects, as hypothesised by previous studies. Our results suggest that the environmental setting of experiments (community structure, nutrient availability and occurrence of phytoplankton growth) is a key factor determining the TEP response to pCO2 perturbations.

Under-ice export flux measurements by short-term drifting sediment traps at 27 stations in the Arctic Ocean during summer 1995, 1997, and 2012

A critical question regarding the organic carbon cycle in the Arctic Ocean is whether the decline in ice extent and thickness and the associated increase in solar irradiance in the upper ocean will result in increased primary production and particulate organic carbon (POC) export. To assess spatial and temporal variability in POC export, under-ice export fluxes were measured with short-term sediment traps in the northern Laptev Sea in July-August-September 1995, north of the Fram Strait in July 1997, and in the Central Arctic in August-September 2012. Sediment traps were deployed at 2-5 m and 20-25 m under ice for periods ranging from 8.5 to 71 h. In addition to POC fluxes, total particulate matter, chlorophyll a, biogenic particulate silica, phytoplankton, and zooplankton fecal pellet fluxes were measured to evaluate the amount and composition of the material exported in the upper Arctic Ocean. Whereas elevated export fluxes observed on and near the Laptev Sea shelf were likely the combined result of high primary production, resuspension, and release of particulate matter from melting ice, low export fluxes above the central basins despite increased light availability during the record minimum ice extent of 2012 suggest that POC export was limited by nutrient supply during summer. These results suggest that the ongoing decline in ice cover affects export fluxes differently on Arctic shelves and over the deep Arctic Ocean and that POC export is likely to remain low above the central basins unless additional nutrients are supplied to surface waters.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

A New Clustering Methodology for the Analysis of Sorted or Categorized Stimuli

This paper introduces a new stochastic clustering methodology devised for the analysis of categorized or sorted data. The methodology reveals consumers' common category knowledge as well as individual differences in using this knowledge for classifying brands in a designated product class. A small study involving the categorization of 28 brands of U.S. automobiles is presented where the results of the proposed methodology are compared with those obtained from KMEANS clustering. Finally, directions for future research are discussed.

Short-term Trading in Long-term Funds: Implications for Financial Managers

Most hospitality firms do not consider managing stock portfolios to be a main part of their operations. They are in the service business, using their real assets and the services provided by employees to create valuable experiences for guests. However, the need to focus on stock investments arises through those employees. Employees consistently rank benefits, including retirement benefits, among the top five contributors to job satisfaction and as a key consideration in accepting a job.1 It is not surprising, then, that more than 90 percent of companies with 500 or more employees offer retirement plans. The five largest hotel companies in the U.S. have over $10 billion in assets under management in their retirement plans, making these plans a key component in retirement investment decisions.

Using the interaural time difference and cross-correlation to localise short-term complex noises

The mammalian binaural cue of interaural time difference (ITD) and cross-correlation have long been used to determine the point of origin of a sound source. The ITD can be defined as the different points in time at which a sound from a single location arrives at each individual ear [1]. From this time difference, the brain can calculate the angle of the sound source in relation to the head [2]. Cross-correlation compares the similarity of each channel of a binaural waveform producing the time lag or offset required for both channels to be in phase with one another. This offset corresponds to the maximum value produced by the cross-correlation function and can be used to determine the ITD and thus the azimuthal angle θ of the original sound source. However, in indoor environments, cross-correlation has been known to have problems with both sound reflections and reverberations. Additionally, cross-correlation has difficulties with localising short-term complex noises when they occur during a longer duration waveform, i.e. in the presence of background noise. The crosscorrelation algorithm processes the entire waveform and the short-term complex noise can be ignored. This paper presents a technique using thresholding which enables higher-localisation abilities for short-term complex sounds in the midst of background noise. To determine the success of this thresholding technique, twenty-five sounds were recorded in a dynamic and echoic environment. The twenty-five sounds consist of hand-claps, finger-clicks and speech. The proposed technique was compared to the regular cross-correlation function for the same waveforms, and an average of the azimuthal angles determined for each individual sample. The sound localisation ability for all twenty-five sound samples is as follows: average of the sampled angles using cross-correlation: 44%; cross-correlation technique with thresholding: 84%. From these results, it is clear that this proposed technique is very successful for the localisation of short-term complex sounds in the midst of background noise and in a dynamic and echoic indoor environment.

Principles and Practice of Fire Modelling: A Collection of Lecture Notes for a Short Course

Once the preserve of university academics and research laboratories with high-powered and expensive computers, the power of sophisticated mathematical fire models has now arrived on the desk top of the fire safety engineer. It is a revolution made possible by parallel advances in PC technology and fire modelling software. But while the tools have proliferated, there has not been a corresponding transfer of knowledge and understanding of the discipline from expert to general user. It is a serious shortfall of which the lack of suitable engineering courses dealing with the subject is symptomatic, if not the cause. The computational vehicles to run the models and an understanding of fire dynamics are not enough to exploit these sophisticated tools. Too often, they become 'black boxes' producing magic answers in exciting three-dimensional colour graphics and client-satisfying 'virtual reality' imagery. As well as a fundamental understanding of the physics and chemistry of fire, the fire safety engineer must have at least a rudimentary understanding of the theoretical basis supporting fire models to appreciate their limitations and capabilities. The five day short course, "Principles and Practice of Fire Modelling" run by the University of Greenwich attempt to bridge the divide between the expert and the general user, providing them with the expertise they need to understand the results of mathematical fire modelling. The course and associated text book, "Mathematical Modelling of Fire Phenomena" are aimed at students and professionals with a wide and varied background, they offer a friendly guide through the unfamiliar terrain of mathematical modelling. These concepts and techniques are introduced and demonstrated in seminars. Those attending also gain experience in using the methods during "hands-on" tutorial and workshop sessions. On completion of this short course, those participating should: - be familiar with the concept of zone and field modelling; - be familiar with zone and field model assumptions; - have an understanding of the capabilities and limitations of modelling software packages for zone and field modelling; - be able to select and use the most appropriate mathematical software and demonstrate their use in compartment fire applications; and - be able to interpret model predictions. The result is that the fire safety engineer is empowered to realise the full value of mathematical models to help in the prediction of fire development, and to determine the consequences of fire under a variety of conditions. This in turn enables him or her to design and implement safety measures which can potentially control, or at the very least reduce the impact of fire.

Search Based Clustering for Protecting Software with Diversified Updates

Reverse engineering is usually the stepping stone of a variety of at-tacks aiming at identifying sensitive information (keys, credentials, data, algo-rithms) or vulnerabilities and flaws for broader exploitation. Software applica-tions are usually deployed as identical binary code installed on millions of com-puters, enabling an adversary to develop a generic reverse-engineering strategy that, if working on one code instance, could be applied to crack all the other in-stances. A solution to mitigate this problem is represented by Software Diversity, which aims at creating several structurally different (but functionally equivalent) binary code versions out of the same source code, so that even if a successful attack can be elaborated for one version, it should not work on a diversified ver-sion. In this paper, we address the problem of maximizing software diversity from a search-based optimization point of view. The program to protect is subject to a catalogue of transformations to generate many candidate versions. The problem of selecting the subset of most diversified versions to be deployed is formulated as an optimisation problem, that we tackle with different search heuristics. We show the applicability of this approach on some popular Android apps.