Intrusion Detection in Industrial Networks via Data Streaming

Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in\ua0 Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations.In this chapter we explain that\ua0 stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to\ua0 facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems).We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment

Interplaying Cassandra NoSQL Consistency and Performance: A Benchmarking Approach

This experience report analyses performance of the Cassandra NoSQL database and studies the fundamental trade-off between data consistency and delays in distributed data storages. The primary focus is on investigating the interplay between the Cassandra performance (response time) and its consistency settings. The paper reports the results of the read and write performance benchmarking for a replicated Cassandra cluster, deployed in the Amazon EC2 Cloud. We present quantitative results showing how different consistency settings affect the Cassandra performance under different workloads. One of our main findings is that it is possible to minimize Cassandra delays and still guarantee the strong data consistency by optimal coordination of consistency settings for both read and write requests. Our experiments show that (i) strong consistency costs up to 25% of performance and (ii) the best setting for strong consistency depends on the ratio of read and write operations. Finally, we generalize our experience by proposing a benchmarking-based methodology for run-time optimization of consistency settings to achieve the maximum Cassandra performance and still guarantee the strong data consistency under mixed workloads

Incidence of WHO stage 3 and 4 conditions following initiation of Anti-Retroviral Therapy in resource limited settings

To determine the incidence of WHO clinical stage 3 and 4 conditions during early anti-retroviral therapy (ART) in resource limited settings (RLS)

Reactive Processing of RDF Streams of Events

Events on the Web are increasingly being produced in the form of data streams, and are present in many different scenarios and applications such as health monitoring, environmental sensing or social networks. The heterogeneity of event streams has raised the challenges of integrating, interpreting and processing them coherently. Semantic technologies have shown to provide both a formal and practical framework to address some of these challenges, producing standards for representation and querying, such as RDF and SPARQL. However, these standards are not suitable for dealing with streams for events, as they do not include the concpets of streaming and continuous processing. The idea of RDF stream processing (RSP) has emerged in recent years to fill this gap, and the research community has produced prototype engines that cover aspects including complex event processing and stream reasoning to varying degrees. However, these existing prototypes often overlook key principles of reactive systems, regarding the event-driven processing, responsiveness, resiliency and scalability. In this paper we present a reactive model for implementing RSP systems, based on the Actor model, which relies on asynchronous message passing of events. Furthermore, we study the responsiveness property of RSP systems, in particular for the delivery of streaming results

The stellar halo of the Galaxy

Stellar halos may hold some of the best preserved fossils of the formation history of galaxies. They are a natural product of the merging processes that probably take place during the assembly of a galaxy, and hence may well be the most ubiquitous component of galaxies, independently of their Hubble type. This review focuses on our current understanding of the spatial structure, the kinematics and chemistry of halo stars in the Milky Way. In recent years, we have experienced a change in paradigm thanks to the discovery of large amounts of substructure, especially in the outer halo. I discuss the implications of the currently available observational constraints and fold them into several possible formation scenarios. Unraveling the formation of the Galactic halo will be possible in the near future through a combination of large wide field photometric and spectroscopic surveys, and especially in the era of Gaia.Comment: 46 pages, 16 figures. References updated and some minor changes. Full-resolution version available at http://www.astro.rug.nl/~ahelmi/stellar-halo-review.pd

Tackling Africa's digital divide

Innovations in ‘sustainable’ photonics technologies such as free-space optical links and solar-powered equipment provide developing countries with new cost-effective opportunities for deploying future-proof telecommunication networks

Dysregulated Nephrin in Diabetic Nephropathy of Type 2 Diabetes: A Cross Sectional Study

Podocyte specific proteins are dysregulated in diabetic nephropathy, though the extent of their expression loss is not identical and may be subject to different regulatory factors. Quantifying the degree of loss may help identify the most useful protein to use as an early biomarker of diabetic nephropathy.Protein expression of synaptopodin, podocin and nephrin were quantified in 15 Type 2 diabetic renal biopsies and 12 control patients. We found statistically significant downregulation of synaptopodin (P<0.0001), podocin (P = 0.0002), and nephrin (P<0.0001) in kidney biopsies of diabetic nephropathy as compared with controls. Urinary nephrin levels (nephrinuria) were then measured in 66 patients with Type 2 diabetes and 10 healthy controls by an enzyme-linked immunosorbent assay (Exocell, Philadelphia, PA). When divided into groups according to normo-, micro-, and macroalbuminuria, nephrinuria was found to be present in 100% of diabetic patients with micro- and macroalbuminuria, as well as 54% of patients with normoalbuminuria. Nephrinuria also correlated significantly with albuminuria (rho = 0.89, p<0.001), systolic blood pressure (rho = 0.32, p = 0.007), and correlated negatively with serum albumin (rho = -0.48, p<0.0001) and eGFR (rho = -0.33, p = 0.005).These data suggest that key podocyte-specific protein expressions are significantly and differentially downregulated in diabetic nephropathy. The finding that nephrinuria is observed in a majority of these normoalbuminuric patients demonstrates that it may precede microalbuminuria. If further research confirms nephrinuria to be a biomarker of pre-clinical diabetic nephropathy, it would shed light on podocyte metabolism in disease, and raise the possibility of new and earlier therapeutic targets

Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data).We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats