A taxonomy and survey of attacks against machine learning

The majority of machine learning methodologies operate with the assumption that their environment is benign. However, this assumption does not always hold, as it is often advantageous to adversaries to maliciously modify the training (poisoning attacks) or test data (evasion attacks). Such attacks can be catastrophic given the growth and the penetration of machine learning applications in society. Therefore, there is a need to secure machine learning enabling the safe adoption of it in adversarial cases, such as spam filtering, malware detection, and biometric recognition. This paper presents a taxonomy and survey of attacks against systems that use machine learning. It organizes the body of knowledge in adversarial machine learning so as to identify the aspects where researchers from different fields can contribute to. The taxonomy identifies attacks which share key characteristics and as such can potentially be addressed by the same defence approaches. Thus, the proposed taxonomy makes it easier to understand the existing attack landscape towards developing defence mechanisms, which are not investigated in this survey. The taxonomy is also leveraged to identify open problems that can lead to new research areas within the field of adversarial machine learning.     

A survey on fog computing for the Internet of Things

Fog computing has emerged to support the requirements of IoT applications that could not be met by today’s solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.     

A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems

Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.     

Application of molecular identification tools for Lactobacillus, with a focus on discrimination between closely related species: A review

Lactobacillus is among the most important GRAS food lactic acid bacteria, with nearly 140 species at present, mostly of industrial importance. Being part of the natural flora of a range of food products like raw milk, fermented dairy products, fruits, vegetables, meat products they also serve as starters for a number of fermented food products either to enhance the quality or to add health benefits. These groups of economically important species are often alike in phenotypic and physiological characteristics, probably due to their co-evolution in the same ecological niches; hence they are difficult to be differentiated. This demands advanced methods for their proper identification and characterization. With the advancement of molecular biology, a range of DNA-based molecular techniques has replaced the largely cumbersome phenotypic methods. This review summarizes the various molecular techniques available for detection and identification within the genus Lactobacillus, with special emphasis on the four groups of closely resembling species: L. casei group, L. acidophilus group, L. delbrueckii subspecies, and L. plantarum group. This review also provides insights into current trends for alternative molecular markers other than 16S rRNA to resolve the ambiguity within phylogenetically close species in the genus Lactobacillus.     

Revision of the needle-shaped Fragilaria species (Fragilariaceae,Bacillariophyta) in the Laurentian Great Lakes (United States of America,Canada)

We investigated several common, needle-shaped diatoms to better characterize the taxonomy of the genus Fragilaria in the Laurentian Great Lakes. We conducted diatom morphometric analysis facilitated by SEM and LM imaging on samples collected as a part of the USEPA’s long-term biological monitoring program. We resolved several decades-long taxonomic problems in the Great Lakes. The results indicated that previous records of species formerly belonging to the genus Synedra, such as S. (Fragilaria) ostenfeldii, S. (Fragilaria) radians, and S. filiformis, should be corrected as these species likely do not occur in the Great Lakes. Valve morphology confirmed the presence of four previously undescribed species: Fragilaria andreseniana sp. nov., Fragilaria stoermeriana sp. nov., Fragilaria limnetica sp. nov., and Fragilaria michiganensis sp. nov. The morphology of several other Fragilaria taxa in the Great Lakes was examined, including a teratologic taxon (Fragilaria sp. 1), Fragilaria crotonensis, Fragilaria grunowii, and a taxon showing morphological affinity to Fragilaria lemanensis (reported here as Fragilaria cf. lemanensis). The spatial and seasonal distributions of these needle-shaped taxa were also analyzed and discussed.     

Nucleic acid techniques in bacterial systematics and identification

Systematics and identification play central roles in any discipline of microbiology. The current prokaryotic taxonomic framework as proposed in Bergey's Manual of Systematic Bacteriology is mainly based on small subunit rRNA data. Alternative markers representing the conserved core of the prokaryotic genomes roughly support rRNA based phylogenetic inference. Consequently, many of the nucleic acid based techniques for identification target these molecules: i.e. comparative sequencing, specific probing, diagnostic PCR, and pattern techniques. For studies at species and lower taxonomic ranks, however, alternative less conserved targets have to be chosen. An overview of commonly used targets and methods for identification or differentiation is given below.     

Enterococci as probiotics and their implications in food safety

Enterococci belong to the lactic acid bacteria (LAB) and they are of importance in foods due to their involvement in food spoilage and fermentations, as well as their utilisation as probiotics in humans and slaughter animals. However, they are also important nosocomial pathogens that cause bacteraemia, endocarditis and other infections. Some strains are resistant to many antibiotics and possess virulence factors such as adhesins, invasins, pili and haemolysin. The role of enterococci in disease has raised questions on their safety for use in foods or as probiotics. Studies on the incidence of virulence traits among enterococcal strains isolated from food showed that some can harbour virulence traits, but it is also thought that virulence is not the result of the presence of specific virulence determinants alone, but is rather a more intricate process. Specific genetic lineages of hospital-adapted strains have emerged, such as E. faecium clonal complex (CC) 17 and E. faecalis CC2, CC9, CC28 and CC40, which are high risk enterococcal clonal complexes. These are characterised by the presence of antibiotic resistance determinants and/or virulence factors, often located on pathogenicity islands or plasmids. Mobile genetic elements thus are considered to play a major role in the establishment of problematic lineages. Although enterococci occur in high numbers in certain types of fermented cheeses and sausages, they are not deliberately added as starter cultures. Some E. faecium and E. faecalis strains are used as probiotics and are ingested in high numbers, generally in the form of pharmaceutical preparations. Such probiotics are administered to treat diarrhoea, antibiotic-associated diarrhoea or irritable bowel syndrome, to lower cholesterol levels or to improve host immunity. In animals, enterococcal probiotics are mainly used to treat or prevent diarrhoea, for immune stimulation or to improve growth. From a food microbiological point of view, the safety of the bacteria used as probiotics must be assured, and data on the major strains in use so far indicate that they are safe. The advantage of use of probiotics in slaughter animals, from a food microbiological point of view, lies in the reduction of zoonotic pathogens in the gastrointestinal tract of animals which prevents the transmission of these pathogens via food. The use of enterococcal probiotics should, in view of the development of problematic lineages and the potential for gene transfer in the gastrointestinal tract of both humans and animals, be carefully monitored, and the advantages of using these and new strains should be considered in a well contemplated risk/benefit analysis.     

Human error in European air traffic management: the HERA project

The majority of accidents in hazardous activities are caused by human error. This problem is not new, and a good deal of research, application, and development of practical techniques for the analysis, prediction and reduction of human errors or their negative effects, has occurred in a range of industries. Whilst human error within flight operations has for some time been the centre of exhaustive research and debate, a similar analysis within the field of air traffic management (ATM) is not so comprehensive. Therefore, it may be that ATM can learn from other industries.This paper deals with an approach to ATM incident analysis that is being developed in the European ATM arena. This new approach aims to determine how and why human errors are contributing to incidents, and thus how to improve human reliability within a high-reliability system. This developing approach is called ‘HERA’ — Human Error in ATM Project.The paper reports on a formative part, or phase 1, of a project that reviewed the theoretical and practical literature to determine the best conceptual framework upon which to base an ATM incident analysis tool. The conceptual framework chosen is that of human performance from an information processing perspective, which has been adapted to make it more contextually relevant to ATM. A prototype structure was adopted for a technique with which to analyse ATM incidents. This paper summarises the review of the literature surveyed, and briefly describes the structure of the prototype technique.     

Pattern recognition by polynomial canonical regression

A pattern is conceived as an equivalence class of samples in a criterion space. Two cases are distinguished: (1) The number of classes is very small in comparison to the number of samples; (2) it approximates the number of samples.Prediction of class membership by means of predictor variables in the first case is replaced by identification of a small vicinity of a sample in the second one.Linear and approximations of nonlinear canonical regression functions are proposed for both issues. Preprocessing of data by Karhunen-Loève expansion is discussed.Empirical results concerning classification of schematic faces and identification of photographic portraits are presented.     

基于树型结构的多层网络攻击事件分类方法

网络攻击事件的分类是对网络攻击进行深入研究的基础,本文提出了基于树型结构的多层网络攻击分类方法(NMATTS),并进行了描述以及特点的分析。