160 research outputs found
Deployment of Next Generation Intrusion Detection Systems against Internal Threats in a Medium-sized Enterprise
In this increasingly digital age, companies struggle to understand the origin of cyberattacks. Malicious actions can come from both the outside and the inside the business, so it is necessary to adopt tools that can reduce cyber risks by identifying the anomalies when the first symptoms appear.
This thesis deals with the topic of internal attacks and explains how to use innovative Intrusion Detection Systems to protect the IT infrastructure of Medium-sized Enterprises.
These types of technologies try to solve issues like poor visibility of network traffic, long response times to security breaches, and the use of inefficient access control mechanisms.
In this research, multiple types of internal threats, the different categories of Intrusion Detection Systems and an in-depth analysis of the state-of-the-art IDSs developed during the last few years have been detailed. After that, there will be a brief explanation of the effectiveness of IDSs in both testing and production environments.
All the reported phases took place within a company network, starting from the positioning of the IDS, moving on to its configuration and ending with the production environment.
There is an analysis of the company expectations, together with an explanation of the different IDSs characteristics.
This research shows data about potential attacks, mitigated and resolved threats, as well as network changes made thanks to the information gathered while using a cutting edge IDS.
Moreover, the characteristics that a medium-sized company must have in order to be adequately protected by a new generation IDS have been generalized. In the same way, the functionalities that an IDS must possess in order to achieve the set objectives were reported. IDSs are incredibly adaptable to different environments, such as companies of different sectors and sizes, and can be tuned to achieve better results.
At the end of this document are reported the potential future developments that should be addressed to improve IDS technologies further
Models and Algorithms for the Optimisation of Replenishment, Production and Distribution Plans in Industrial Enterprises
Tesis por compendio[ES] La optimización en las empresas manufactureras es especialmente importante, debido a las grandes inversiones que realizan, ya que a veces estas inversiones no obtienen el rendimiento esperado porque los márgenes de beneficio de los productos son muy ajustados. Por ello, las empresas tratan de maximizar el uso de los recursos productivos y financieros minimizando el tiempo perdido y, al mismo tiempo, mejorando los flujos de los procesos y satisfaciendo las necesidades del mercado.
El proceso de planificación es una actividad crÃtica para las empresas. Esta tarea implica grandes retos debido a los cambios del mercado, las alteraciones en los procesos de producción dentro de la empresa y en la cadena de suministro, y los cambios en la legislación, entre otros.
La planificación del aprovisionamiento, la producción y la distribución desempeña un papel fundamental en el rendimiento de las empresas manufactureras, ya que una planificación ineficaz de los proveedores, los procesos de producción y los sistemas de distribución contribuye a aumentar los costes de los productos, a alargar los plazos de entrega y a reducir los beneficios. La planificación eficaz es un proceso complejo que abarca una amplia gama de actividades para garantizar que los equipos, los materiales y los recursos humanos estén disponibles en el momento y el lugar adecuados.
Motivados por la complejidad de la planificación en las empresas manufactureras, esta tesis estudia y desarrolla herramientas cuantitativas para ayudar a los planificadores en los procesos de la planificación del aprovisionamiento, producción y distribución. Desde esta perspectiva, se proponen modelos realistas y métodos eficientes para apoyar la toma de decisiones en las empresas industriales, principalmente en las pequeñas y medianas empresas (PYMES).
Las aportaciones de esta tesis suponen un avance cientÃfico basado en una exhaustiva revisión bibliográfica sobre la planificación del aprovisionamiento, la producción y la distribución que ayuda a comprender los principales modelos y algoritmos utilizados para resolver estos planes, y pone en relieve las tendencias y las futuras direcciones de investigación. También proporciona un marco holÃstico para caracterizar los modelos y algoritmos centrándose en la planificación de la producción, la programación y la secuenciación. Esta tesis también propone una herramienta de apoyo a la decisión para seleccionar un algoritmo o método de solución para resolver problemas concretos de la planificación del aprovisionamiento, producción y distribución en función de su complejidad, lo que permite a los planificadores no duplicar esfuerzos de modelización o programación de técnicas de solución. Por último, se desarrollan nuevos modelos matemáticos y enfoques de solución de última generación, como los algoritmos matheurÃsticos, que combinan la programación matemática y las técnicas metaheurÃsticas.
Los nuevos modelos y algoritmos comprenden mejoras en términos de rendimiento computacional, e incluyen caracterÃsticas realistas de los problemas del mundo real a los que se enfrentan las empresas de fabricación. Los modelos matemáticos han sido validados con un caso de una importante empresa del sector de la automoción en España, lo que ha permitido evaluar la relevancia práctica de estos novedosos modelos utilizando instancias de gran tamaño, similares a las existentes en la empresa objeto de estudio. Además, los algoritmos matheurÃsticos han sido probados utilizando herramientas libres y de código abierto. Esto también contribuye a la práctica de la investigación operativa, y proporciona una visión de cómo desplegar estos métodos de solución y el tiempo de cálculo y rendimiento de la brecha que se puede obtener mediante el uso de software libre o de código abierto.[CA] L'optimització a les empreses manufactureres és especialment important, a causa de les grans inversions que realitzen, ja que de vegades aquestes inversions no obtenen el rendiment esperat perquè els marges de benefici dels productes són molt ajustats. Per això, les empreses intenten maximitzar l'ús dels recursos productius i financers minimitzant el temps perdut i, alhora, millorant els fluxos dels processos i satisfent les necessitats del mercat.
El procés de planificació és una activitat crÃtica per a les empreses. Aquesta tasca implica grans reptes a causa dels canvis del mercat, les alteracions en els processos de producció dins de l'empresa i la cadena de subministrament, i els canvis en la legislació, entre altres.
La planificació de l'aprovisionament, la producció i la distribució té un paper fonamental en el rendiment de les empreses manufactureres, ja que una planificació ineficaç dels proveïdors, els processos de producció i els sistemes de distribució contribueix a augmentar els costos dels productes, allargar els terminis de lliurament i reduir els beneficis. La planificació eficaç és un procés complex que abasta una à mplia gamma d'activitats per garantir que els equips, els materials i els recursos humans estiguen disponibles al moment i al lloc adequats.
Motivats per la complexitat de la planificació a les empreses manufactureres, aquesta tesi estudia i desenvolupa eines quantitatives per ajudar als planificadors en els processos de la planificació de l'aprovisionament, producció i distribució. Des d'aquesta perspectiva, es proposen models realistes i mètodes eficients per donar suport a la presa de decisions a les empreses industrials, principalment a les petites i mitjanes empreses (PIMES).
Les aportacions d'aquesta tesi suposen un avenç cientÃfic basat en una exhaustiva revisió bibliogrà fica sobre la planificació de l'aprovisionament, la producció i la distribució que ajuda a comprendre els principals models i algorismes utilitzats per resoldre aquests plans, i posa de relleu les tendències i les futures direccions de recerca. També proporciona un marc holÃstic per caracteritzar els models i algorismes centrant-se en la planificació de la producció, la programació i la seqüenciació. Aquesta tesi també proposa una eina de suport a la decisió per seleccionar un algorisme o mètode de solució per resoldre problemes concrets de la planificació de l'aprovisionament, producció i distribució en funció de la seua complexitat, cosa que permet als planificadors no duplicar esforços de modelització o programació de tècniques de solució. Finalment, es desenvolupen nous models matemà tics i enfocaments de solució d'última generació, com ara els algoritmes matheurÃstics, que combinen la programació matemà tica i les tècniques metaheurÃstiques.
Els nous models i algoritmes comprenen millores en termes de rendiment computacional, i inclouen caracterÃstiques realistes dels problemes del món real a què s'enfronten les empreses de fabricació. Els models matemà tics han estat validats amb un cas d'una important empresa del sector de l'automoció a Espanya, cosa que ha permés avaluar la rellevà ncia prà ctica d'aquests nous models utilitzant instà ncies grans, similars a les existents a l'empresa objecte d'estudi. A més, els algorismes matheurÃstics han estat provats utilitzant eines lliures i de codi obert. Això també contribueix a la prà ctica de la investigació operativa, i proporciona una visió de com desplegar aquests mètodes de solució i el temps de cà lcul i rendiment de la bretxa que es pot obtindre mitjançant l'ús de programari lliure o de codi obert.[EN] Optimisation in manufacturing companies is especially important, due to the large investments they make, as sometimes these investments do not obtain the expected return because the profit margins of products are very tight. Therefore, companies seek to maximise the use of productive and financial resources by minimising lost time and, at the same time, improving process flows while meeting market needs.
The planning process is a critical activity for companies. This task involves great challenges due to market changes, alterations in production processes within the company and in the supply chain, and changes in legislation, among others.
Planning of replenishment, production and distribution plays a critical role in the performance of manufacturing companies because ineffective planning of suppliers, production processes and distribution systems contributes to higher product costs, longer lead times and less profits. Effective planning is a complex process that encompasses a wide range of activities to ensure that equipment, materials and human resources are available in the right time and the right place.
Motivated by the complexity of planning in manufacturing companies, this thesis studies and develops quantitative tools to help planners in the replenishment, production and delivery planning processes. From this perspective, realistic models and efficient methods are proposed to support decision making in industrial companies, mainly in small- and medium-sized enterprises (SMEs).
The contributions of this thesis represent a scientific breakthrough based on a comprehensive literature review about replenishment, production and distribution planning that helps to understand the main models and algorithms used to solve these plans, and highlights trends and future research directions. It also provides a holistic framework to characterise models and algorithms by focusing on production planning, scheduling and sequencing. This thesis also proposes a decision support tool for selecting an algorithm or solution method to solve concrete replenishment, production and distribution planning problems according to their complexity, which allows planners to not duplicate efforts modelling or programming solution techniques. Finally, new state-of-the-art mathematical models and solution approaches are developed, such as matheuristic algorithms, which combine mathematical programming and metaheuristic techniques.
The new models and algorithms comprise improvements in computational performance terms, and include realistic features of real-world problems faced by manufacturing companies. The mathematical models have been validated with a case of an important company in the automotive sector in Spain, which allowed to evaluate the practical relevance of these novel models using large instances, similarly to those existing in the company under study. In addition, the matheuristic algorithms have been tested using free and open-source tools. This also helps to contribute to the practice of operations research, and provides insight into how to deploy these solution methods and the computational time and gap performance that can be obtained by using free or open-source software.This work would not have been possible without the following funding sources: Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana for hiring predoctoral research staff with Grant (ACIF/2018/170) and the European Social Fund with the Grant Operational Programme of FSE 2014-2020. Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana for predoctoral contract students to stay in research centers outside the research centers outside the Valencian Community (BEFPI/2021/040) and the European Social Fund.Guzmán Ortiz, BE. (2022). Models and Algorithms for the Optimisation of Replenishment, Production and Distribution Plans in Industrial Enterprises [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/187461Compendi
Augmentative communication device design, implementation and evaluation
The ultimate aim of this thesis was to design and implement an advanced software based Augmentative Communication Device (ACD) , or Voice Output Communication Aid NOCA), for non-vocal Learning Disabled individuals by applying current psychological models, theories, and experimental techniques. By taking account of potential user's cognitive and linguistic abilities a symbol based device (Easy Speaker) was produced which outputs naturalistic digitised human speech and sound and makes use of a photorealistic symbol set. In order to increase the size of the available symbol set a hypermedia style dynamic screen approach was employed. The relevance of the hypermedia metaphor in relation to models of knowledge representation and language processing was explored.Laboratory based studies suggested that potential user's could learn to productively operate the software, became faster and more efficient over time when performing set conversational tasks. Studies with unimpaired individuals supported the notion that digitised speech was less cognitively demanding to decode, or listen to.With highly portable, touch based, PC compatible systems beginning to appear it is hoped that the otherwise silent will be able to use the software as their primary means of communication with the speaking world. Extensive field trials over a six month period with a prototype device and in collaboration with user's caregivers strongly suggested this might be the case.Off-device improvements were also noted suggesting that Easy Speaker, or similar software has the potential to be used as a communication training tool. Such training would be likely 10 improve overall communicative effectiveness.To conclude, a model for successful ACD development was proposed
A comparative analysis of algorithms for satellite operations scheduling
Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration
Emergence in the security of protocols for mobile ad-hoc networks
This thesis is concerned with the study of secure wireless routing protocols, which have been deployed for the purpose of exchanging information in an adhoc networking enviromnent.
A discrete event simulator is developed, utilising an adaptive systems modelling approach and emergence that aims to assess networking protocols in the presence of adversarial behaviour. The model is used in conjunction with the characteristics that routing protocols have and also a number of cryptographic primitives that can be deployed in order to safeguard the information being exchanged. It is shown that both adversarial behaviour, as well as protocol descriptions can be described in a way that allows for them to be treated as input on the machine level.
Within the system, the output generated selects the fittest protocol design capable of withstanding one or more particular type of attacks. As a result, a number of new and improved protocol specifications are presented and benchmarked against conventional metrics, such as throughput, latency and delivery criteria. From this process, an architecture for designing wireless routing protocols based on a number of security criteria is presented, whereupon the decision of using particular characteristics in a specification has been passed onto the machine level
Developing collaborative planning support tools for optimised farming in Western Australia
Land-use (farm) planning is a highly complex and dynamic process. A land-use plan can be optimal at one point in time, but its currency can change quickly due to the dynamic nature of the variables driving the land-use decision-making process. These include external drivers such as weather and produce markets, that also interact with the biophysical interactions and management activities of crop production.The active environment of an annual farm planning process can be envisioned as being cone-like. At the beginning of the sowing year, the number of options open to the manager is huge, although uncertainty is high due to the inability to foresee future weather and market conditions. As the production year reveals itself, the uncertainties around weather and markets become more certain, as does the impact of weather and management activities on future production levels. This restricts the number of alternative management options available to the farm manager. Moreover, every decision made, such as crop type sown in a paddock, will constrains the range of management activities possible in that paddock for the rest of the growing season.This research has developed a prototype Land-use Decision Support System (LUDSS) to aid farm managers in their tactical farm management decision making. The prototype applies an innovative approach that mimics the way in which a farm manager and/or consultant would search for optimal solutions at a whole-farm level. This model captured the range of possible management activities available to the manager and the impact that both external (to the farm) and internal drivers have on crop production and the environment. It also captured the risk and uncertainty found in the decision space.The developed prototype is based on a Multiple Objective Decision-making (MODM) - á Posteriori approach incorporating an Exhaustive Search method. The objective set used for the model is: maximising profit and minimising environmental impact. Pareto optimisation theory was chosen as the method to select the optimal solution and a Monte Carlo simulator is integrated into the prototype to incorporate the dynamic nature of the farm decision making process. The prototype has a user-friendly front and back end to allow farmers to input data, drive the application and extract information easily
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