Security Risk Management for the IoT systems

Alates 2012. aastast on ülemaailmne infastruktuuri üksuste arv (The Internet of Things) jõudsalt kasvanud üle kahe korra. Koos selle numbriga on ka kasvanud ka võimalikud riskid ning ohud, mis mõjutavad süsteemi turvalisust. Tulemuseks on suur hulk isiklikke andmeid kas varastatud või kahjustatud. Vastavalt allikatele "Third Quarter, 2016 State of the Internet / Security Report" ja "Akamai Intelligent Platform", on DdoS Q3 rünnakute arv suurenenud 2016 aastal 71% võrreldes aastaga 2015. Kõige suurem DdoS fikseeritud rünnakutest oli 623 Gbps rünnak. Kõik need faktid tõestavad, et Iot süsteemis on veel siiamaani probleeme isikuandmete turvalisusega. Isklikud andmed on ohtude suhtes haavatavad. Käesolev töö ühendab Iot raamastikus turvalisuse riskijuhtimine teadmised olemasoleva praktikaga. Raamastiku eesmärgiks on tugevdada Iot süsteemi nõrku osi ning kaitsta isiklikke andmeid. Pakume välja esialgse igakülgse võrdlusmudeli juhtkontrolli turvariskideks IoT süsteemides hallatavate ja kontrollitavate info- ja andmevarade jaoks. Infosüsteemide turvalisuse riskijuhtimise valdkonna domeeni mudeli põhjal uurime, kuidas avatud veebirakenduse turvalisuse projektis määratletud turvaauke ja nende vastumeetmeid võiks vaadelda IoT kontekstis. Selleks, et illustreerida etalonmudeli rakendamist, katsetatakse raamistikku IoT-süsteemil. Sellesse süsteemi kuuluvad Raspberry Pi 3, sensorid ning kaugandmete ladustamine.Since 2012 the number of units in global infrastructure for the information society (The Internet of Things) has grown twice. With this number also has grown the number of possible threats and risks, which influence security on all levels of the system. As a result, a huge amount of users' data was stolen or damaged. According to Third Quarter, 2016 State of the Internet / Security Report based on data gathered from the Akamai Intelligent Platform the total number of DDoS attacks in Q3 2016 increased in 71\\% compared to Q3 2015. With 623 Gbps data transfer attack it was largest DDoS ever and this fact will only increase the number of future attack events. All these facts reveal a problem that a lot of IoT systems are still unsecured and users' data or personal information stay vulnerable to threats. The thesis combines knowledge of Security Risk Management with existing practice in securing in IoT into a framework, which aim is to cover vulnerabilities in IoT systems in order to protect users' data. We propose an initial comprehensive reference model to management security risks to the information and data assets managed and controlled in the IoT systems. Based on the domain model for the information systems security risk management, we explore how the vulnerabilities and their countermeasures defined in the open Web application security project could be considered in the IoT context. To illustrate the applicability of the reference model we test the framework on self-developed IoT system represented by Raspberry Pi 3 interconnected with sensors and remote data storage

A Fully Automated Robot for the Preparation of Fungal Samples for FTIR Spectroscopy Using Deep Learning

Manual preparation of fungal samples for Fourier Transform Infrared (FTIR) spectroscopy involves sample washing, homogenization, concentration and spotting, which requires time-consuming and repetitive operations, making it unsuitable for screening studies. This paper presents the design and development of a fully automated robot for the preparation of fungal samples for FTIR spectroscopy. The whole system was constructed based on a previously-developed ultrasonication robot module, by adding a newly-designed centrifuge module and a newly-developed liquid handling module. The liquid handling module consists of a high accuracy electric pipette for spotting and a low accuracy syringe pump for sample washing and concentration. A dual robotic arm system with a gripper connects all of the hardware components. Furthermore, a camera on the liquid handling module uses deep learning to identify the labware settings, which includes the number and positions of well plates and pipette tips. Machine vision on the ultrasonication robot module can detect the sample wells and return the locations to the liquid handling module, which makes the system hand-free for users. Tight integration of all the modules enables the robot to process up to two 96-well microtiter (MTP) plates of samples simultaneously. Performance evaluation shows the deep learning based approach can detect four classes of labware with high average precision, from 0.93 to 1.0. In addition, tests of all procedures show that the robot is able to provide homogeneous sample spots for FTIR spectroscopy with high positional accuracy and spot coverage rate

Oleaginous yeasts respond differently to carbon sources present in lignocellulose hydrolysate

Background Microbial oils, generated from lignocellulosic material, have great potential as renewable and sustainable alternatives to fossil-based fuels and chemicals. By unravelling the diversity of lipid accumulation physiology in different oleaginous yeasts grown on the various carbon sources present in lignocellulose hydrolysate (LH), new targets for optimisation of lipid accumulation can be identified. Monitoring lipid formation over time is essential for understanding lipid accumulation physiology. This study investigated lipid accumulation in a variety of oleaginous ascomycetous and basidiomycetous strains grown in glucose and xylose and followed lipid formation kinetics of selected strains in wheat straw hydrolysate (WSH). Results Twenty-nine oleaginous yeast strains were tested for their ability to utilise glucose and xylose, the main sugars present in WSH. Evaluation of sugar consumption and lipid accumulation revealed marked differences in xylose utilisation capacity between the yeast strains, even between those belonging to the same species. Five different promising strains, belonging to the species Lipomyces starkeyi, Rhodotorula glutinis, Rhodotorula babjevae and Rhodotorula toruloides, were grown on undiluted wheat straw hydrolysate and lipid accumulation was followed over time, using Fourier transform-infrared (FTIR) spectroscopy. All five strains were able to grow on undiluted WSH and to accumulate lipids, but to different extents and with different productivities. R. babjevae DVBPG 8058 was the best-performing strain, accumulating 64.8% of cell dry weight (CDW) as lipids. It reached a culture density of 28 g/L CDW in batch cultivation, resulting in a lipid content of 18.1 g/L and yield of 0.24 g lipids per g carbon source. This strain formed lipids from the major carbon sources in hydrolysate, glucose, acetate and xylose. R. glutinis CBS 2367 also consumed these carbon sources, but when assimilating xylose it consumed intracellular lipids simultaneously. Rhodotorula strains contained a higher proportion of polyunsaturated fatty acids than the two tested Lipomyces starkeyi strains. Conclusions There is considerable metabolic diversity among oleaginous yeasts, even between closely related species and strains, especially when converting xylose to biomass and lipids. Monitoring the kinetics of lipid accumulation and identifying the molecular basis of this diversity are keys to selecting suitable strains for high lipid production from lignocellulose

Montana Kaimin, January 30, 2008

Student newspaper of the University of Montana, Missoula.https://scholarworks.umt.edu/studentnewspaper/6138/thumbnail.jp

Assessment of genetically modified maize Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA-GMO-DE-2018-149)

Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 was produced by conventional breeding of the GM maize events Bt11, MIR162, MIR604, MON 89034, 5307 and GA21. Accordingly, Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 maize produces the transgenic proteins in the individual GM maize events (Cry1Ab, PAT, Vip3Aa20, PMI, mCry3A, MIR604 PMI, Cry1A.105, Cry2Ab2, eCry3.1Ab and mEPSPS). Event Bt11 maize expresses the insecticidal protein Cry1Ab that protects against feeding damage caused by certain lepidopteran pests and the phosphinothricin acetyltransferase (PAT) protein for weed control by providing tolerance to herbicide products containing glufosinate ammonium. Event MIR162 maize expresses the insecticidal protein Vip3Aa20 that protects against feeding damage caused by certain lepidopteran pests and the PMI protein which enables transformed plant cells to utilise mannose as a primary carbon source and therefore used as a selectable marker in the development of the MIR162 maize. Event MIR604 maize expresses the insecticidal protein mCry3A that protects against feeding damage caused by certain coleopteran pests and the MIR604 PMI protein which enables transformed plant cells to utilise mannose as a primary carbon source and therefore used as a selectable marker in the development of the MIR604 maize. Event MON 89034 maize expresses the insecticidal proteins Cry1A.105 and Cry2Ab2 that protect against feeding damage caused by certain lepidopteran pests. Event 5307 maize expresses the insecticidal protein eCry3.1Ab that protects against feeding damage caused by certain coleopteran pests and the PMI protein which enables transformed plant cells to utilise mannose as a primary carbon source and therefore used as a selectable marker in the development of the 5307 maize. Event GA21 expresses the double-mutated 5-enolpyruvylshikimate-3-phosphate synthase enzyme (mEPSPS) for weed control by providing tolerance to herbicide products containing glyphosate.The scientific documentation provided in the application for genetically modified maize Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 is adequate for risk assessment, and in accordance with EFSA guidance on risk assessment of genetically modified plants for use in food or feed. The VKM GMO panel does not consider the introduced modifications in maize Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 to imply potential specific health or environmental risks in Norway, compared to EU-countries The EFSA opinion is adequate also for Norwegian considerations. Therefore, a full risk assessment of maize Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 was not performed by the VKM GMO Panel.Assessment of genetically modified maize Bt11 x MIR162 x MIR604 x MON 89034 x 5307 x GA21 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA-GMO-DE-2018-149)publishedVersio

Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganisms

Background Monitoring and control of both growth media and microbial biomass is extremely important for the development of economical bioprocesses. Unfortunately, process monitoring is still dependent on a limited number of standard parameters (pH, temperature, gasses etc.), while the critical process parameters, such as biomass, product and substrate concentrations, are rarely assessable in-line. Bioprocess optimization and monitoring will greatly benefit from advanced spectroscopy-based sensors that enable real-time monitoring and control. Here, Fourier transform (FT) Raman spectroscopy measurement via flow cell in a recirculatory loop, in combination with predictive data modeling, was assessed as a fast, low-cost, and highly sensitive process analytical technology (PAT) system for online monitoring of critical process parameters. To show the general applicability of the method, submerged fermentation was monitored using two different oleaginous and carotenogenic microorganisms grown on two different carbon substrates: glucose fermentation by yeast Rhodotorula toruloides and glycerol fermentation by marine thraustochytrid Schizochytrium sp. Additionally, the online FT-Raman spectroscopy approach was compared with two at-line spectroscopic methods, namely FT-Raman and FT-infrared spectroscopies in high throughput screening (HTS) setups. Results The system can provide real-time concentration data on carbon substrate (glucose and glycerol) utilization, and production of biomass, carotenoid pigments, and lipids (triglycerides and free fatty acids). Robust multivariate regression models were developed and showed high level of correlation between the online FT-Raman spectral data and reference measurements, with coefficients of determination (R2) in the 0.94–0.99 and 0.89–0.99 range for all concentration parameters of Rhodotorula and Schizochytrium fermentation, respectively. The online FT-Raman spectroscopy approach was superior to the at-line methods since the obtained information was more comprehensive, timely and provided more precise concentration profiles. Conclusions The FT-Raman spectroscopy system with a flow measurement cell in a recirculatory loop, in combination with prediction models, can simultaneously provide real-time concentration data on carbon substrate utilization, and production of biomass, carotenoid pigments, and lipids. This data enables monitoring of dynamic behaviour of oleaginous and carotenogenic microorganisms, and thus can provide critical process parameters for process optimization and control. Overall, this study demonstrated the feasibility of using FT-Raman spectroscopy for online monitoring of fermentation processes.Raman spectroscopy online monitoring of biomass production, intracellular metabolites and carbon substrates during submerged fermentation of oleaginous and carotenogenic microorganismspublishedVersio

Assessment of genetically modified maize MON 95379 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA‐GMO‐NL‐2020‐170)

Event MON 95379 is a genetically modified maize developed by a two-step process. In the first step, immature embryos of maize inbred line LH244 were co-cultured with a disarmed Agrobacterium tumefaciens (also known as Rhizobium radiobacter) strain ABI containing the vector PV-ZMIR522223. In the second step, selected R2 lines were crossed with maize inbred LH244 line expressing Crerecombinase, which had been transformed with vector PVZMOO513642. In the resulting plants, the CP4 EPSPS-cassette (used for selection of transformed plants) was excised by the Cre recombinase, and the Cre gene was subsequently segregated away, through conventional breeding, to obtain maize MON 95379. Maize MON 95379 expresses Cry1B.868, a chimeric protein containing domains from Cry1A, Cry1B and Cry1C naturally expressed in Bacillus thuringiensis, and Cry1Da_7, an optimised version of Cry1Da carrying four amino acids substitutions to increase its activity. The two Cry proteins expressed in maize MON 95379 provide protection against targeted pests within the order of butterflies and moths (Lepidoptera) including fall armyworm (Spodoptera frugiperda), sugarcane borer (Diatraea saccharalis) and corn earworm (Helicoverpa zea). The scientific documentation provided in the application for genetically modified maize MON 95379 is adequate for risk assessment, and in accordance with EFSA guidance on risk assessment of genetically modified plants for use in food or feed. The VKM GMO panel does not consider the introduced modifications in event MON 95379 to imply potential specific health or environmental risks in Norway, compared to EU-countries. The EFSA opinion is adequate also for Norwegian considerations. Therefore, a full risk assessment of event MON 95379 was not performed by the VKM GMO Panel.Assessment of genetically modified maize MON 95379 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA‐GMO‐NL‐2020‐170)publishedVersio

Assessment of genetically modified maize DP41143 x MON890343 x MON 874113 x DAS40278-9 and sub-combinations for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA GMO-NL-2020-171)

Genetically modified maize DP41149 x MON 890349 x MON 874119 x DAS-40278-9 was developed by crossing to combine four single events: DP4114, MON 89034, MON 87411 and DAS-40278-9. DP4114 express the Cry1F protein to confer protection against certain lepidopteran pests, the Cry34Ab1 and Cry35Ab1 proteins to confer protection against certain coleopteran pests and PAT protein to confer tolerance to glufosinate-ammonium-containing herbicides. MON 89034 express the Cry1A.105 and Cry2Ab2 proteins to confer protection against certain lepidopteran pests. MON 87411 express the Cry3Bb1 protein to confer protection against certain coleopteran larvae and the DvSnf7 dsRNA confer protection against western corn rootworm, and the CP4 EPSPS protein for tolerance to glyphosate containing herbicides. DAS-40278-9 express the AAD-1 protein to catalyse the degradation of the general class ofherbicides known as aryloxyphenoxypropionates (AOPP) and to confer tolerance to 2,4- dichlorophenoxyacetic acid (2,4-D) herbicides.Assessment of genetically modified maize DP41143 x MON890343 x MON 874113 x DAS40278-9 and sub-combinations for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (application EFSA GMO-NL-2020-171)publishedVersionpublishedVersio

Assessment of genetically modified soybean MON 87701 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (renewal application EFSA-GMO-RX-021)

Event MON 87701 is a genetically modified soybean developed via Agrobacterium tumefaciens transformation. MON 87701 plants contain the transgene cry1Ac which encodes the protein Cry1Ac. The protein Cry1Ac provides resistance against specific lepidopteran pests. The scientific documentation provided in the renewal application (EFSA-GMO-RX-021) for soybean MON 87701 is adequate for risk assessment, and in accordance with EFSA guidance on risk assessment of genetically modified plants for use in food or feed. The VKM GMO panel does not consider the introduced modifications in soybean MON 87701 to imply potential specific health or environmental risks in Norway, compared to EU-countries. The EFSA opinion is adequate also for Norwegian considerations. Therefore, a full risk assessment of event MON 87701 was not performed by the VKM GMO Panel.Assessment of genetically modified soybean MON 87701 for food and feed uses, import and processing under Regulation (EC) No 1829/2003 (renewal application EFSA-GMO-RX-021)publishedVersio