Navigating Cyberthreat Intelligence with CYBEX-P: Dashboard Design and User Experience

As the world’s data exponentially grows, two major problems increasingly need to be solved. The first is how to interpret large and complex datasets so that actionable insight can be achieved. The second is how to effectively protect these data and the assets they represent. This thesis’ topic lies at the intersection of these two crucial issues. The research presented in the thesis learns from past work on applying data visualization to multiple domains, with a focus on cybersecurity visualization. These learnings were then applied to a new research area: cybersecurity information sharing. The frontend considerations for CYBEX-P, a cybersecurity information sharing platform developed at UNR, are discussed in detail. A user-facing web application was developed from these requirements, resulting in an approachable, highly visual cyberthreat investigation tool. The threat-intelligence graph at the center of this dashboard-style tool allows analysts to interact with indicators of compromise and efficiently reach security conclusions. In addition to research and related software development, a user study was conducted with participants from cybersecurity backgrounds to test different visualization configurations. Subsequent analysis revealed that the misuse of simple visual properties can lead to perilous reductions in accuracy and response-time. Recommendations are provided for avoiding these pitfalls and balancing information density. The study results inform the final functionalities of the CYBEX-P front end and serve as a foundation for similar prospective tools. By improving how insights can be extracted from large cybersecurity datasets, the work presented in the thesis paves the way towards a more secure and informed future in a technology-driven world

Modelagem de abundância com drones : detectabilidade, desenho amostral e revisão automática de imagens em um estudo com cervos-do-pantanal

Entender como a abundância de uma espécie se distribui no espaço e/ou no tempo é uma questão fundamental em ecologia e conservação e ajuda, por exemplo, a elucidar relações entre a heterogeneidade de paisagens e populações ou compreender influência de predação na distribuição de indivíduos. Informações de tamanho populacional também são essenciais para avaliar risco de extinção, monitorar populações ameaçadas e planejar ações de conservação. Modelar a abundância de cervos-do-pantanal (Blastocerus dichotomus), sendo um grande herbívoro da América do Sul, pode ser importante para entender relações da espécie com a variação espacial da produtividade primária, das áreas úmidas que a espécie ocupa e do seu principal predador, a onça- pintada. Além disso, por estar ameaçado de extinção, estimar a abundância de cervos pode contribuir para avaliar populações relictuais da espécie, assim como monitorar populações após grandes eventos, como os incêndios de 2020 no Pantanal. Porém, acessar estimativas de abundância confiáveis de maneira eficiente requer métodos robustos que levem em conta os possíveis erros nas contagens e que forneçam as estimativas em tempo hábil, além de um desenho amostral otimizado para aproveitar os recursos geralmente escassos. Os drones tem aparecido como uma ferramenta versátil e custo-efetiva para amostragem de populações animais e vêm sendo aplicados para várias espécies diferentes nos mais variados contextos ecológicos. Como um método emergente, o uso de drones na ecologia fornece oportunidades para explorar novas possibilidades de amostragem e análise de dados, ao mesmo tempo em que pode apresentar novos desafios. Nesta tese, i) exploro oportunidade e desafios na utilização de drones para modelagem de abundância de animais, abordando questões de erros de detecção, desenho amostral e como lidar com os grandes bancos de imagens gerados; e ii) aplico os métodos desenvolvidos para estudar a variação na abundância de cervo-do- pantanal, assim como estabelecer uma abordagem para monitoramento robusto e efetivo dessa espécie. Assim, no primeiro capítulo, conduzo uma revisão na literatura descrevendo os potenciais erros de detecção que podem enviesar estimativas de abundância com drones, buscando soluções atuais para lidar com esses erros e identificando lacunas que precisam de desenvolvimento. Nessa revisão, destaco o potencial dos modelos hierárquicos para estimar abundância em amostragens com drone. No segundo capítulo, aplico amostragens espaço-temporalmente replicadas com drone, analisadas com modelos hierárquicos N-mixture, para entender o efeito de processos topo-base (distribuição de onças-pintadas) e base-topo (disponibilidade de forragem de qualidade e corpos d’água) na distribuição da abundância de cervos-do- pantanal. Nesse estudo, encontrei que, na época seca, os cervos se concentram em áreas de alta qualidade (maior disponibilidade de forragem e próximas a corpos d’água), mesmo sendo a região em que é esperado maior efeito da predação. No capítulo 3, em um estudo com simulações, avalio o desempenho de modelos N-mixture para estimativas de abundância a partir de amostragens espaço-temporalmente replicadas, explorando otimização de esforço amostral e o impacto de um protocolo com observadores duplos na acurácia das estimativas. No capítulo 4, desenvolvo uma abordagem para estimar abundância com drone usando observadores múltiplos na revisão das imagens, sendo um dos observadores baseado em um processamento semiautomático usando algoritmos de inteligência artificial. Nesse estudo, exploro técnicas de aprendizado profundo de máquina, com redes neurais convolucionais, acessíveis para ecólogos, treinando algoritmos para detectar cervos nas imagens de drone. Além de ajudar a elucidar questões sobre as relações do cervo-do-pantanal com aspectos diferentes da paisagem do Pantanal, as abordagens exploradas e desenvolvidas aqui têm um grande potencial de aplicação, ajudando a estabelecer os drones como uma ferramenta eficiente para modelagem e monitoramento populacional de diversas espécies animais, e particularmente de cervos.Understanding how abundance distributes in space and/or time is a fundamental question in ecology and conservation, and it helps, for example, to elucidate relationships between landscape heterogeneity or predation and populations. Information on the population size also is essential to evaluate extinction risk, monitor threatened species and plan conservation actions. Abundance modeling of marsh deer (Blastocerus dichotomus), as a large herbivore of South America, may be important to understand the relationships of this species with spatial variation in primary productivity, in the availability of wetlands that the species inhabits, and in the distribution of its main predator, the jaguar. Moreover, since marsh deer threatened to extinction, estimating its abundance can be contribute in assessments of relictual populations, as well as in monitoring the species after big events, such as the Pantanal 2020 megafires. However, efficiently assessing reliable abundance estimates require robust methods that account for possible sources of error in counts while providing the estimates timely. An optimized sampling design is also important, in order to make the best use of the usual scarce resources. Drones have raised as a versatile and cost- effective tool for sampling animal populations, and they have been applied for several species in a wide variety of ecological contexts. As being an emergent method, the use of drones in ecology provides opportunities to explore novel possibilities of sampling and analyzing data, while potentially presenting new challenges. In this thesis I: i) explore opportunities and challenges in the use of drones for animal abundance modeling, approaching issues about detection errors, sampling design and how to deal with the huge image sets generated from drone flights; and ii) apply the developed methods to study the spatial variation in marsh deer abundance and to establish an approach to monitor this species robustly and efficiently. Thus, in the first chapter, I carry on a literature review describing potential sources of errors that may bias abundance estimation with drones and the current solutions to address them, identifying gaps that need development. In this review, I highlight the potential of hierarchical models for abundance estimations from drone-based surveys. In the second chapter, I apply spatiotemporally replicated drone surveys, analyzed with N-mixture models, to understand the influence of bottom-up (forage and water) and top-down (jaguar density) variables on the spatial variation of marsh deer local abundance. In such study, I found that, in the dry season, the deer concentrate in high quality areas (high-quality forage available and close to water bodies), even these regions being expected to present higher predation risks. In chapter 3, in a simulation study, I evaluate the performance of N- mixture models for abundance estimation from spatiotemporally replicated surveys, exploring optimization of sampling effort and the impact of a double-observer protocol on estimation accuracy. In chapter 4, I develop a pipeline to estimate abundance from drone-based surveys using a multiple-observer protocol in which one of the observer is a semiautomated procedure based on deep learning algorithms. In such study, I explore deep learning techniques with convolutional neural networks that are accessible for ecologists, and train algorithms to detect marsh deer in drone imagery. Besides helping to elucidate questions about the relationships of marsh deer with landscape variables in Pantanal, the approaches explored and developed here have a great potential of application in order to establish drones as an efficient technique for population modeling and monitoring of several wildlife species, and particularly the marsh deer

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection

How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of problems and must strive to capture the knowledge necessary for solving each new task it encounters. If the agent is capable of accumulating knowledge in some form of compositional representation, it could then selectively reuse and combine relevant pieces of knowledge to construct novel solutions. Despite the intuitive appeal of this simple idea, the literatures on lifelong learning and compositional learning have proceeded largely separately. In an effort to promote developments that bridge between the two fields, this article surveys their respective research landscapes and discusses existing and future connections between them

WiFi Miner: An online apriori and sensor based wireless network Intrusion Detection System

This thesis proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms. The proposed system, WiFi Miner, solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm also proposed by this thesis. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, thereby improving efficiency and run times significantly. A positive anomaly score is assigned to each packet (record) for each infrequent pattern found while a negative anomaly score is assigned for each frequent pattern found. So, a record with final positive anomaly score is considered as anomaly based on the presence of more infrequent patterns than frequent patterns found

DABI: A data base for image analysis with nondeterministic inference capability

A description is given of the data base used in the perception subsystem of the Mars robot vehicle prototype being implemented at the Jet Propulsion Laboratory. This data base contains two types of information. The first is generic (uninstantiated, abstract) information that specifies the general rules of perception of objects in the expected environments. The second kind of information is a specific (instantiated) description of a structure, i.e., the properties and relations of objects in the specific case being analyzed. The generic knowledge can be used by the approximate reasoning subsystem to obtain information on the specific structures which is not directly measurable by the sensory instruments. Raw measurements are input either from the sensory instruments or a human operator using a CRT or a TTY

Multivariate discretization of continuous valued attributes.

The area of Knowledge discovery and data mining is growing rapidly. Feature Discretization is a crucial issue in Knowledge Discovery in Databases (KDD), or Data Mining because most data sets used in real world applications have features with continuously values. Discretization is performed as a preprocessing step of the data mining to make data mining techniques useful for these data sets. This thesis addresses discretization issue by proposing a multivariate discretization (MVD) algorithm. It begins withal number of common discretization algorithms like Equal width discretization, Equal frequency discretization, Naïve; Entropy based discretization, Chi square discretization, and orthogonal hyper planes. After that comparing the results achieved by the multivariate discretization (MVD) algorithm with the accuracy results of other algorithms. This thesis is divided into six chapters, covering a few common discretization algorithms and tests these algorithms on a real world datasets which varying in size and complexity, and shows how data visualization techniques will be effective in determining the degree of complexity of the given data set. We have examined the multivariate discretization (MVD) algorithm with the same data sets. After that we have classified discrete data using artificial neural network single layer perceptron and multilayer perceptron with back propagation algorithm. We have trained the Classifier using the training data set, and tested its accuracy using the testing data set. Our experiments lead to better accuracy results with some data sets and low accuracy results with other data sets, and this is subject ot the degree of data complexity then we have compared the accuracy results of multivariate discretization (MVD) algorithm with the results achieved by other discretization algorithms. We have found that multivariate discretization (MVD) algorithm produces good accuracy results in comparing with the other discretization algorithm