A Survey on Data-driven Network Intrusion Detection

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

Assessment of changes in Grenache grapevine maturity in a Mediterranean context over the last half-century

This study aims to i) evaluate some descriptive variables for Grenache berry composition over the last 50 years in the southern Rhône Valley wine-growing region and ii) analyse the impacts of climate on the main annual developmental phases of the Grenache berry to understand recent changes observed in the vineyard. A large and spatialised historical, open database from the Rhône Valley grape maturity network (1969–2020) was used to explore trends in grape profile during maturity and at harvest. Then, gridded climate data was used for processing phenological stages and ecoclimatic indicators. Significant changes in grapevine phenology and maturity dynamics were found and linked with changes to ecoclimatic indicators by carrying out a correlation analysis. Depending on the phenological phases, a limited number of ecoclimatic indicators had a significant effect on the maturity profile. The results highlight direct climate impacts on different maturity and yield variables over the last 50 years. These results provide important information about future issues in grape production and the implications for managing viticulture adaptation strategies and thus serve as a basis for assessing, prioritising and optimising technical means of maintaining current grape quality and yield.This study uses an ecoclimatic approach for examining in detail the effects of climate change on the Grenache grape variety in a Mediterranean context. The open database provides the latest information from a large network of plots and over a long period of time, making it possible to validate many results recorded in the literature. This is the first study to use this open database and we wish this database could lead to further explorations and results in viticulture and climate change issues.

Environmental Filtering by pH and Salinity Jointly Drives Prokaryotic Community Assembly in Coastal Wetland Sediments

Understanding the microbial community assembly is an essential topic in microbial ecology. Coastal wetlands are an important blue carbon sink, where microbes play a key role in biogeochemical cycling of nutrients and energy transformation. However, the drivers controlling the distribution patterns and assembly of bacterial and archaeal communities in coastal wetland are unclear. Here we examined the diversity, co-occurrence network, assembly processes and environmental drivers of bacterial and archaeal communities from inshore to offshore sediments by the sequencing of 16S rRNA gene amplicons. The value of α- and β-diversity of bacterial and archaeal communities generally did not change significantly (P > 0.05) between offshore sites, but changed significantly (P < 0.05) among inshore sites. Sediment pH and salinity showed significant effects on the diversity and keystone taxa of bacterial and archaeal communities. The bacterial and archaeal co-occurrence networks were inextricably linked with pH and salinity to formed the large network nodes, suggesting that they were the key factors to drive the prokaryotic community. We also identified that heterogeneous and homogeneous selection drove the bacterial and archaeal community assembly, while the two selections became weaker from offshore sites to inshore sites, suggesting that deterministic processes were more important in offshore sites. Overall, these results suggested that the environmental filtering of pH and salinity jointly governed the assembly of prokaryotic community in offshore sediments. This study advances our understanding of microbial community assembly in coastal wetland ecosystems.

Mathematical Modelling of Twin -T Notch Filter using Floating Admittance Matrix

A novel article presents the RC-notch filter function using the floating admittance matrix approach. The main advantages of the approach underlined the easy implementation and effective computation. The proposed floating admittance matrix (FAM) method is unique, and the same can be used for all types of electronic circuits. This method takes advantage of the partitioning technique for a large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation at the first instant itself. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering can analyze any circuit to derive all types of transfer functions. The mathematical modelling using the FAM method allows the designer to adjust their design at any stage of analysis comfortably. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits.

A simple approach towards estimating solar irradiance

Solar radiation is or vital interest in characterizing an area with respect to its agricultural potential. However, these are not readily available for a large network. An attempt. has been made to deduce solar irradiance from climatic data, such as temperature range. Based on daily data of Pune for 1986-90, a relationship has been developed between atmospheric transmittance and the daily range of air temperature. The model developed has been tested on independent data and found to give fairly accurate estimation of solar irradiance. Nearly 70% of the variation in daily solar radiation could be explained by this simple method. The effect of solar irradiance on microclimate has also been discussed. The model developed has been tested for Hyderabad and Calcutta and found to give encouraging results.

Sensitivity, Hazard, and Vulnerability of Farmlands to Saltwater Intrusion in Low-Lying Coastal Areas of Venice, Italy

Saltwater intrusion is a growing threat for coastal aquifers and agricultural practices in low-lying plains. Most of the farmlands located between the margin of the Southern Venice lagoon and the Northern Po delta, Italy, lie a few meters below mean sea level and are drained by a large network of artificial channels and hydraulic infrastructures to avoid frequent flooding and allow agricultural practices. This work proposes an assessment of the vulnerability to saltwater intrusion, following a new concept of the hazard status, resulting in combining the depth of the freshwater/saltwater interface and the electrical resistivity of the shallow subsoil. The sensitivity of the farmland system was assessed by using ground elevation, distance from freshwater and saltwater sources, permeability, potential runoff, land subsidence, and sea-level rise indicators. Relative weights were assigned by a pairwise comparison following the Analytic Hierarchy Process approach. The computed vulnerability map highlights that about 30% of the farmlands is under strong and extreme conditions, 28% between marginal and moderate, and 40% under negligible conditions. Results from previous vulnerability assessments are discussed in order to explain their differences in terms of hazard status conceptualization and sensitivity characterization of farmland system.

Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research

BackgroundThe Federal Ministry of Research and Education funded the Network of University Medicine for establishing an infrastructure for pandemic research. This includes the development of a COVID-19 Data Exchange Platform (CODEX) that provides standardised and harmonised data sets for COVID-19 research. Nearly all university hospitals in Germany are part of the project and transmit medical data from the local data integration centres to the CODEX platform. The medical data on a person that has been collected at several sites is to be made available on the CODEX platform in a merged form. To enable this, a federated trusted third party (fTTP) will be established, which will allow the pseudonymised merging of the medical data. The fTTP implements privacy preserving record linkage based on Bloom filters and assigns pseudonyms to enable re-pseudonymisation during data transfer to the CODEX platform.ResultsThe fTTP was implemented conceptually and technically. For this purpose, the processes that are necessary for data delivery were modelled. The resulting communication relationships were identified and corresponding interfaces were specified. These were developed according to the specifications in FHIR and validated with the help of external partners. Existing tools such as the identity management system E-PIX® were further developed accordingly so that sites can generate Bloom filters based on person identifying information. An extension for the comparison of Bloom filters was implemented for the federated trust third party. The correct implementation was shown in the form of a demonstrator and the connection of two data integration centres.ConclusionsThis article describes how the fTTP was modelled and implemented. In a first expansion stage, the fTTP was exemplarily connected through two sites and its functionality was demonstrated. Further expansion stages, which are already planned, have been technically specified and will be implemented in the future in order to also handle cases in which the privacy preserving record linkage achieves ambiguous results. The first expansion stage of the fTTP is available in the University Medicine network and will be connected by all participating sites in the ongoing test phase.

Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients

Background Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc. Purpose This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level. Patients and methods A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support. Results The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically. Conclusion With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered (“personalized medication landscape “) and to identify critical patients even before problems are reported (“medication alert”). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine.

Recommendation algorithm based on attributed multiplex heterogeneous network

In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.