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Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area. Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.

Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria, and because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective tool in assessing and identifying phishing websites than any other traditional tool since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the `fuzziness¿ in traditional website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing websites. The proposed model is based on FL operators which is used to characterize the website phishing factors and indicators as fuzzy variables and produces six measures and criteria¿s of website phishing attack dimensions with a layer structure. Our experimental results showed the significance and importance of the phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing website rate.

This paper presents a Call Admission Control (CAC) algorithm based fuzzy logic to maintain the quality of service using cell breathing concept. When a new call is accepted by a cell, its current user is generally affected due to cell breathing. The proposed CAC algorithm accepts a new call only if the current users in the cell are not jeopardized. Performance evaluation is done for single-cell and multicell scenarios. In multicell scenario dynamic assignment of users to the neighboring cell, so called handoff, has been considered to achieve a lower blocking probability. Handoff and new call requests are assumed with handoff being given preference using a reserved channel scheme. CAC for different types of services are shown which depend upon the bandwidth requirement for voice, data and video. Distance, arrival rate, bandwidth and nonorthogonality factor of the signal are considered for making the call acceptance decision. The paper demonstrates that fuzzy logic with the cell breathing concept can be used to develop a CAC algorithm to achieve a better performance evaluation.

Phishing techniques have not only grown in number, but also in sophistication. Phishers might have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of the phishing attacks, which are mainly on-line banking consumers and payment service providers, are facing substantial financial loss and lack of trust in Internet-based services. In order to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge and experience. So far, various solutions have been proposed and developed to address these problems. Most of these approaches are not able to make a decision dynamically on whether the site is in fact phished, giving rise to a large number of false positives. This is mainly due to limitation of the previously proposed approaches, for example depending only on fixed black and white listing database, missing of human intelligence and experts, poor scalability and their timeliness. In this research we investigated and developed the application of an intelligent fuzzy-based classification system for e-banking phishing website detection. The main aim of the proposed system is to provide protection to users from phishers deception tricks, giving them the ability to detect the legitimacy of the websites. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with association classification mining algorithms. The approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy structure has been constructed for all gathered and extracted phishing website features and patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack type. To reduce human knowledge intervention, Different classification and association algorithms have been implemented to generate fuzzy phishing rules automatically, to be integrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify all relevant fuzzy rules from the training data set. A comparative study and analysis showed that the proposed learning approach has a higher degree of predictive and detective capability than existing models. Experiments also showed significance of some important phishing criteria like URL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested and validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results obtained are promising and showed that our intelligent fuzzy based classification detection system can provide an effective help for real-time phishing website detection. The toolbar successfully recognized and detected approximately 92% of the phishing websites selected from our test data set, avoiding many miss-classified websites and false phishing alarms.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications. / The full-text was made available at the end of the embargo period on 29th Sept 2017.

Hacking information systems is continuously on the increase. Social engineering attacks is performed by manipulating the weakest link in the security chain; people. Consequently, this type of attack has gained a higher rate of success than a technical attack. Based in Expert Systems, this study proposes a proactive and integrated Intelligent Social Engineering Security Model to mitigate the human risk and reduce the impact of social engineering attacks. Many computer users do not have enough security knowledge to be able to select a strong password for their authentication. The author has attempted to implement a novel quantitative approach to achieve strong passwords. A new fuzzy logic tool is being developed to evaluate password strength and measures the password strength based on dictionary attack, time crack and shoulder surfing attack (social engineering). A comparative study of existing tools used by major companies such as Microsoft, Google, CertainKey, Yahoo and Facebook are used to validate the proposed model and tool. A comprehensive literature survey and analytical study performed on phishing emails representing social engineering attacks that are directly related to financial fraud are presented and compared with other security threats. This research proposes a novel approach that successfully addresses social engineering attacks. Another intelligent tool is developed to discover phishing messages and provide educational feedback to the user focusing on the visible part of the incoming emails, considering the email’s source code and providing an in-line awareness security feedback.

[pt] O Sistema elétrico brasileiro é baseado quase que integralmente em energia produzida por Usinas Hidroelétricas. Estas Máquinas Hidroelétricas possuem um comportamento diferenciado das máquinas turbo geradoras, cujo comportamento já foi identificado e classificado em pesquisas anteriores. Este trabalho investiga o uso de um Sistema baseado em Conhecimento para o diagnóstico precoce de falhas em Máquinas Hidrogeradoras, visando redução de custos advindos principalmente de paradas operacionais não necessárias na máquina para manutenção. O sistema foi criado com informação obtida a partir dos seguintes meios: sistemática de manutenção executada nas Usinas Hidroelétricas, através de entrevistas à equipe responsável pela manutenção da usina de Furnas (MG); da identificação do comportamento eletromecânico da máquina; e do estudo de casos. O Sistema é capaz de identificar, a partir dos sensores localizados nas máquinas, eventuais falhas, permitindo executar paradas programadas de maneira otimizada. Foi criado um protótipo de um sistema computacional baseado em Conhecimento implementando tal modelo de forma bem flexível. A modelagem criada, a implementação do protótipo computacional e, principalmente, a explicação do raciocínio empregado, agregado com a possibilidade da modificação do conhecimento através da aquisição automática, são contribuições inovadoras deste trabalho. É descrito o Domínio do Problema de diagnosticar falhas em Máquinas Hidrogeradoras, identificado durante análise das informações coletadas da equipe de manutenção na usina de Furnas e de especialistas no comportamento eletromecânico das máquinas. É descrito também o modelo simbólico criado, representativo do domínio, utilizando interface projetada, visando a implementação prática nas usinas. É apresentado uma solução de desacoplamento das informações advindas dos sensores eletromecânicos da máquina e o sistema, através de módulo baseado em Lógica Nebulosa (Fuzzy Logic) que converte as informações numéricas em informações simbólicas compreendidas pelo sistema de diagnóstico, permitindo o uso do sistema, sem alteração em máquinas que possuam características diversas. Finalmente, é apresentada a metodologia de testes adotada para validação do modelo implementado através da simulação de dados de vibração e oscilação, cujo relacionamento com eventuais falhas é parcialmente conhecido, assim como uma conclusão sobre a viabilidade e praticidade de um modelo simbólico na solução do diagnóstico das máquinas hidrogeradoras. Durante o desenvolvimento da tese verificou-se que o conhecimento sobre falhas em Máquinas Hidrogeradoras ainda não está consolidado e que então, um Sistema baseado em Conhecimento com aquisição de conhecimento automático mostra-se uma excelente ferramenta de modelagem para os especialistas. / [en] The Brazilian Electrical Energy supply is almost entirely based on the energy produced by the Hydroeletric Power Station Machines. These Hydroeletric Machines own particular behavior in comparison to the turbogenerator behavior. This work investigates the use of Knowledge based system Hydroeletric Machines fault diagnosis. The system was modeled using information obtained by: the maintenance s systematic executed Hydroeletric Power Stations, though Furnas (Minas Gerais) maintenance team interviews; the Machine electromechanical behavior; and a Case Based study. The system is able to identify, from machine located sensors data analysis, eventual faults, allowing the execution of programmed operational interrupts in the machine in a optimized manner. A computational prototype and, mainly, the interface explain engine in addition to the knowledge modification through acquisition, are the innovative contributions of this work. The machine fault diagnosis problem domain is described, identified in the information, collected from the maintenance team and the electromechanical behavior experts, analysis. It is also described the projected symbolic model, the domain representation, using graphical and friendly interface, aiming its practical implementation in real Power Stations. It is shown a sensor information detach solution, through a Fuzzy Logic based module which converts the numerical data in a symbolic one, known by the diagnosis system, allowing its use, without any modification, in a sort of different machines. Finally, it is shown the test methodology adopted for the prototype validation through oscillation data simulation, which relationship with machine faults is partially known, and the symbolic model praticality and feasibility in the Hidrogenerator Diagnosis solution. Through the thesis development, it was verified that the Hydrogenerator fault knowledge wasn t still consolidated. So, the Knowledge Based system with knowledge acquisition became an excelent modeling tool for the domain experts.

The fuzzy inference system has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving rule aggregation relatively underrepresented. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonditivity and exploitation of interactions between rules. Herein, the fuzzy integral, a parametric non-linear aggregation operator, is used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to “unrestricted” fuzzy sets, i.e., subnormal and non-convex, makes this now possible. The roles of two extensions, gFI and the NDFI, are explored and demonstrate when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.

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