Analysis of Trust Methods in Ad-hoc and Sensor Networks

Samoorganizující ad-hoc a senzorové sítě, které postrádají logickou infrastrukturu (prvky sítě nejsou rozdělěny na přístupové a ostatní), nachází uplatnění v civilních a vojenských aplikacích vyžadujícíh vysokou autonomitu (např. vzdálená, nedostupná nebo nepřátelská území). Tento typ sítí se také upřednostňuje před sítěmi s logickou infrastrukturou při nasazení do dynamického prostředí. Bez logické infrastruktury narážíme na nový typ bezpečnostních problémů, jako jsou nucená spolupráce mezi prvky sítě a detekce záškodnických prvků. Bezpečnostní mechanizmy známé z běžných sítí nelze často použít, protože síťové prvky mají velmi omezený výkon i zdroj energie. Jako velmi efektivní bezpečnostní alternativu lze použít inovativní metody založené na důvěře. Tato práce obsahuje ucelenou analýzu a klasifikaci těchto metod. Součástí práce je i tvorba nových simulačních nástrojů. Tyto nástroje poslouží jako platforma pro implementaci a vylepšování současných metod a navíc umožní jednoduchou tvorbu metod zcela nových. V budoucnu by bezpečnostní mechanismy založené na důvěře mohly být implementovány v běžných ad-hoc sítích a tím umožnit jejich masové nasazení nebo rozšíření do nových oblastí použití.The infrastructure-less self-organizing ad-hoc and sensor networks are suitable for autonomous operation in both civil and military areas (e.g. remote, inaccessible and hostile territory). They are preferred to traditional infrastructure-based networks for deployment in dynamic environment. The absence of the infrastructure introduces several new security issues, such as enforcement of cooperation and malicious node detection. The limited computational power and energy of nodes makes the conventional security measures not applicable to these types of networks. The innovative trust-based security approach is one of the most effective alternatives. This work provides comprehensive analysis and classification of trust methods as well as introduces the brand new simulation tools. It contributes to the networking research by providing the means to optimize and enhance the creation of the new methods as well as to improve the existing ones. In the future, efficient trust-based security mechanisms can be implemented in ad-hoc and sensor networks to enable their mass production and introduce several new areas of application

Modelling the Information-Psychological Impact in Social Networks

The paper considers the objects, subjects, purposes, tools, methods and implementation of information-psychological impact (IPI). It suggests a cellular automata model of the diffusion process of information-psychological impact in social networks, the hierarchy of the changes in the states of the subjects of information-psychological impact and the chart of transitions from state to state used in the cellular automaton algorithm. The suggested cellular automaton takes into account the effect of forgetting the information-psychological impact, as well as social and psychological parameters and probabilistic characteristics of the subjects of the social network. It therefore allows for the modelling of the diffusion of the information-psychological impact in the social network. The model can be used to determine the number of subjects affected by the information-psychological impact and the possibility of successful diffusion of the impact. The modelling of the suggested algorithm was performed. The results of the modelling are analysed in the paper

Probabilistic Analysis of the Influence of Staff Qualification and Information-Psychological Conditions on the Level of Systems Information Security

Taking into account the criticality of the “human factor,” the probabilistic approach for analysis is proposed, including: a model for predicting and assessing the level of systems information security, considering random events, including dependent events; model of information-psychological impact on staff; methodical approach for analyzing an influence of staff qualifications and psychological conditions on the level of system information security. The effectiveness of the application is demonstrated by examples

The particle track reconstruction based on deep learning neural networks

One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes. Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip in GEM detectors due to the appearance in them a lot of fake hits caused by extra spurious crossings of fired strips. Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes. On the basis of our previous two-stage approach based on hits preprocessing using directed K-d tree search followed by a deep neural classifier we introduce here two new tracking algorithms. Both algorithms combine those two stages in one while using different types of deep neural nets. We show that both proposed deep networks do not require any special preprocessing stage, are more accurate, faster and can be easier parallelized. Preliminary results of our new approaches for simulated events are presented.Comment: 8 pages, 3 figures, CHEP 2018, the 23rd International Conference on Computing in High Energy and Nuclear Physics, Sofia, Bulgaria on July 9-13, 2018. arXiv admin note: text overlap with arXiv:1811.0600

Multi-functional Diagnostic Method with Tracer-encapsulated Pellet Injection

In order to obtain a better understanding of impurity transport in magnetically confined plasmas, a Tracer-Encapsulated Soild PELlet (TESPEL) has been developed. The essential points of the TESPEL are as follows: the TESPEL has a double-layered structure, and a tracer impurity, the amount of which can be known precisely, is embedded as an inner core. This structure enables us to deposit the tracer impurity locally inside the plasma. From experiences of developing the TESPEL production technique and its injection experiments, it became clear that various plasma properties can be studied by the TESPEL injection. There are not only impurity transport in the plasma but also transport both outside and inside of the magnetic island O-point, heat transport and high-energy neutral particle flux. Therefore, the TESPEL injection has a favorable multi-functional diagnostic capability. Furthermore a Tracer-Encapsulated Cryogenic PELlet (TECPEL) has been also developed. The TECPEL has an advantage over the TESPEL in terms of no existence of carbons in the outer layer. The TECPEL injector was installed at LHD in December 2005, and the preliminary injection experiments have been carried out

The decay energy of the pure s-process nuclide ¹²³ Te

A direct and high-precision measurement of the mass difference of ¹²³Te and ¹²³Sb has been performed with the Penning-trap mass spectrometer SHIPTRAP using the recently introduced phase-imaging ioncyclotron-resonance technique. The obtained mass difference is 51.912(67) keV/c². Using the masses of the neutral ground states and the energy difference between the ionic states an effective half-life of ¹²³Te has been estimated for various astrophysical conditions. A dramatic influence of the electron capture process on the decay properties of ¹²³Te in hot stellar conditions has been discussed

Spatial resolved high-energy particle diagnostic system using time-of-flight neutral particle analyzer in Large Helical Device

The time-of-flight-type neutral particle analyzer has an ability of horizontal scanning from 40 to 100° of the pitch angle. The information from the spatially resolved energy spectrum gives not only the ion temperature but also the information of the particle confinement and the electric field in plasmas. We have been studying the energy distributions at various magnetic configurations in the neutral beam injection (NBI) plasma. The spatially resolved energy spectra can be observed during long discharges of the NBI plasma by continuous scanning of the neutral particle analyzer. The shape of spectra is almost similar from 44° to 53°. However, the spectra from 55° are strongly varied. They reflect the injection pitch angle of the beam. The pitch angle scanning experiment during the long discharge of NBI plasma has also been made under the reversal of the magnetic field direction. NBI2 becomes counter injected with the reversal. We can easily observe the difference between co- and counter injections of NBI. During the electron cyclotron heating in the low-density plasma for the formation of the internal thermal barrier, large neutral particle increase or decease can be observed. The degree of the increase/decrease depends on the energy and the density. The reason for the variation of the particle flux is that the orbit of the trapped particle changes due to the electric field formed by the strong electron cyclotron heating

Solving Data Quality Problems with Desbordante: a Demo

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios

High Energy Particle Measurements during Long Discharge in LHD

The spatial resolved energy spectra can be observed during a long discharge of NBI plasma bycontinuously scanning the neutral particle analyzer. In these discharges, the plasmas are initiated by the ECH heating, after that NBI#2 (Co-injection) sustains the plasma during 40-60 seconds. The scanned pitch angle is from 44 degrees to 74 degrees. The injected neutral beam (hydrogen) energy of NBI#2 is only 130 keV because the original ion source polarity is negative. The shape of spectra is almost similar from 44 degrees to 53 degrees. However the spectra from 55 degrees are strongly varied. It reflects the injection pitch angle of the beam according to the simulation (53 degrees ot R* = 3.75 m in simulation). The beam keeps the pitch angle at incidence until the beam energy becomes to the energy, which the pitch angle scattering is occurred by the energy loss due to the electron collision. The low flux region can be observed around 10-15 keV, which is 15 times of the electron temperature. The energy region may be equal to the energy at which the pitch angle scattering is occurred. At the energy, the particle is scattered by the collision with the plasma ions and some of particles may run away from the plasma because they have a possibility to enter the loss cone. According to the simulation, the loss cone can be expected at the 10 keV with the small angular dependence. The depth of the loss cone is deep at the small pitch angle. The hollow in the spectrum may be concluded to be the loss cone as the tendency is almost agreed with the experimental result