Adversarial Machine Learning in Smart Energy Systems

Smart Energy Systems represent a radical shift in the approach to energy generation and demand, driven by decentralisation of the energy system to large numbers of low-capacity devices. Managing this flexibility is often driven by machine learning, and requires real-time control and aggregation of these devices, involving a diverse set of companies and devices and creating a longer chain of trust. This poses a security risk, as it is sensitive to adversarial machine learning, whereby models are fooled through malicious input, either for financial gain or to cause system disruption. We show the feasibility of such an attack by analysing empirical data of a real system, and propose directions for future research related to detection and defence mechanisms for these kind of attacks

Efficient use of sentinel sites : detection of invasive honeybee pests and diseases in the UK

Sentinel sites, where problems can be identified early or investigated in detail, form an important part of planning for exotic disease outbreaks in humans, livestock and plants. Key questions are: how many sentinels are required, where should they be positioned and how effective are they at rapidly identifying new invasions? The sentinel apiary system for invasive honeybee pests and diseases illustrates the costs and benefits of such approaches. Here, we address these issues with two mathematical modelling approaches. The first approach is generic and uses probabilistic arguments to calculate the average number of affected sites when an outbreak is first detected, providing rapid and general insights that we have applied to a range of infectious diseases. The second approach uses a computationally intensive, stochastic, spatial model to simulate multiple outbreaks and to determine appropriate sentinel locations for UK apiaries. Both models quantify the anticipated increase in success of sentinel sites as their number increases and as non-sentinel sites become worse at detection; however, unexpectedly sentinels perform relatively better for faster growing outbreaks. Additionally, the spatial model allows us to quantify the substantial role that carefully positioned sentinels can play in the rapid detection of exotic invasions

A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control

Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed

Supplementary Material from Efficient use of sentinel sites: detection of invasive honeybee pests and diseases in the UK

Additional information on the models and parameter

Effects of bacterial inactivation methods on downstream proteomic analysis

Inactivation of pathogenic microbial samples is often necessary for the protection of researchers and to comply with local and federal regulations. By its nature, biological inactivation causes changes to microbial samples, potentially affecting observed experimental results. While inactivation-induced damage to materials such as DNA has been evaluated, the effect of various inactivation strategies on proteomic data, to our knowledge, has not been discussed. To this end, we inactivated samples of Yersinia pestis and Escherichia coli by autoclave, ethanol, or irradiation treatment to determine how inactivation changes liquid chromatography-tandem mass spectrometry data quality as well as apparent protein content of cells. Proteomic datasets obtained from aliquots of samples inactivated by different methods were highly similar, with Pearson correlation coefficients ranging from 0.822 to 0.985 and 0.816 to 0.985 for E. coli and Y. pestis, respectively, suggesting that inactivation had only slight impacts on the set of proteins identified. In addition, spectral quality metrics such as distributions of various database search algorithm scores remained constant across inactivation methods, indicating that inactivation does not appreciably degrade spectral quality. Though overall changes resulting from inactivation were small, there were detectable trends. For example, one-sided Fischer exact tests determined that periplasmic proteins decrease in observed abundance after sample inactivation by autoclaving (α=1.71×10−2 for E. coli, α=4.97×10−4 for Y. pestis) and irradiation (α=9.43×10−7 for E. coli, α=1.21×10−5 for Y. pestis) when compared to controls that were not inactivated. Based on our data, if sample inactivation is necessary, we recommend inactivation with ethanol treatment with secondary preference given to irradiation. •Bacterial samples were inactivated by autoclaving, irradiation, and ethanol treatment.•Proteomic data quality was largely unchanged by inactivation treatments.•Observed protein content of cells largely unchanged after inactivation.•Observed changes largely involved periplasmic and/or outer membrane proteins.•Proteomes of ethanol- and irradiation-inactivated samples showed the fewest changes

Risk indicators affecting honeybee colony survival in Europe : one year of surveillance

The first pan-European harmonized active epidemiological surveillance program on honeybee colony mortality (EPILOBEE) was set up across 17 European Member States to estimate honeybee colony mortality over winter and during the beekeeping season. In nine Member States, overwinter losses were higher and statistically different from the empirical level of 10 % under which the level of overwinter mortality was considered as acceptable with usual beekeeping conditions. In four other countries, these losses were lower. Using multivariable Poisson regression models, it was showed that the size of the operation and apiary and the clinically detected varroosis, American foulbrood (AFB), and nosemosis before winter significantly affected 2012-2013 overwinter losses. Clinically detected diseases, the size of the operation and apiary, and the non-participation to a common veterinary treatment significantly affected 2013 summer losses. EPILOBEE was a prerequisite to implement future projects studying risk factors affecting colony health such as multiple and co-exposure to pesticides