No abstract

Several threats are propagated by malicious websites largely classified as phishing. Its function is important information for users with the purpose of criminal practice. In summary, phishing is a technique used on the Internet by criminals for online fraud. The Artificial Neural Networks (ANN) are computational models inspired by the structure of the brain and aim to simulate human behavior, such as learning, association, generalization and abstraction when subjected to training. In this paper, an ANN Multilayer Perceptron (MLP) type was applied for websites classification with phishing characteristics. The results obtained encourage the application of an ANN-MLP in the classification of websites with phishing characteristics.

Resumo O adensamento populacional de grandes centros urbanos como o da Região Metropolitana de São Paulo leva à verticalização e à formação de condomínios onde o consumo de água não é individualizado e dessa forma dificulta ações de uso racional da água, uma vez que cada morador não tem acesso ao seu consumo. Para atender a essa demanda, as empresas de saneamento têm estruturado programas para individualização do consumo de água. A empresa analisada neste trabalho implementou seu produto de individualização de água, porém ocorreram problemas nos processos comerciais, operacionais e de infraestrutura de Tecnologia da Informação que o envolvem. O objetivo deste trabalho foi, então, propor a reestruturação do produto Medição Individualizada em uma empresa de saneamento básico por meio do uso de framework de Arquitetura Corporativa. Optou-se pelo uso da Arquitetura Corporativa por possibilitar o alinhamento da estratégia com a execução, além de também analisar o negócio, os processos, os sistemas de informação e a infraestrutura de informática. Os resultados obtidos com o uso de framework de Arquitetura Corporativa contribuíram de forma positiva para que a proposta de reestruturação do produto fosse realizada de maneira estruturada por meio da visão global de todas as interfaces.

A container crane has the function of transporting containers from one point to another point. The difficulty of this task lies in the fact that the container is connected to the bridge crane by cables, causing an opening angle while the container is being transported, interfering with the operation at high speeds due to oscillation that occurs at the end point, which could cause accidents. Fuzzy logic (FL) is a mathematical theory that aims to allow the modeling of approximate way of thinking, imitating the human ability to make decisions in uncertain and imprecise environments. The Artificial Neural Networks (ANN) models are made of simple processing units, called artificial neurons, which calculate mathematical functions. The aim of the paper was to present a container crane controller pre-project using an artificial neural network type Multilayer Perceptron (MLP) combined with FL, referred to as Neuro Fuzzy Network (NFN).

The growing development of technology, which has been experienced for several decades, caused the area of Information Technology (IT) to have a significant advance in the corporate world. The expansion of IT to different areas of the company has enabled a greater transmission and storage of information. The arrival of personal computers has also been a greater contributor to an increase in storage of information. Due to that, databases and Information Systems where deployed. The managers began to use the databases for specific business areas, which are called Data Marts (DM). Despite the use of Data Marts, the extracted scenarios did not express the updated information in the way management would require, because there was still a time lag between the occurrence of an event and its absorption by the company, which in fact, meant a delay in moving forward with essential and strategic areas of the business. Minimize this lag became a challenge and a necessity for the competitive business world. At the time, the solution adopted, in order to minimize the time lag between the occurrences and the effective absorption of this information, was called Zero Latency Enterprise (ZLE). The aim of this paper is to examine how real time information can be obtained through the implementation of a ZLE in the DM and how, effectively, it would support the strategic decision-making process in organizations. In order to achieve that, a proposal has been put forward to create a model of DM in ZLE with data obtained from the commercial department of a civil engineering supply company. This analysis reveals significant results, supporting the original idea to develop support for decision making.

No abstract

Projects are essential for organizations to transform strategies into results, but uncertain events can impose risks to achieve a certain objective. Risk management aims to support an organization in deciding how to deal with risks, prioritizing them through the application of Risk Matrices (RMs). RMs or Probability and Impact Matrices is used to support decision-making, helping management to classify and prioritize risks to decide which will be ad-dressed, monitored, or tolerated. RMs are supposedly easy to build and explain, but according to the literature they may contain uncertainties. To deal with uncertainty, it is recommended to apply a Fuzzy Inference System, based on Fuzzy Set Theory (FST) or a Fuzzy Neural Inference System with the presence of an artificial neural network. Thus, the aim of this paper was to develop and apply a Fuzzy Inference System (FIS) and a Fuzzy Neural Inference System (FNIS) in the classification of MRs in projects to reduce uncertainty. The analysis of the results indicated that the application of the two systems resulted in a continuous classification rule by smoothing the boundary areas between each of the RM classes, reducing uncertainty and improving risk classification. Both systems showed good results in reducing uncertainty. However, the results obtained with FNIS were more consistent. The main contribution of this work lies in the possibility of improving the decision making by reducing the uncertainty present in RMs.

In the initial phase of the pentest, named Open Source Intelligence, we use passive recognition with Google Hacking. Google Hacking is a practice that uses strings called Dorks. To support them, the Google Hacking Database is available with thousands of Dorks. However, the Google Hacking Database contains a reduced number of attributes, all with textual values, which makes it impossible to apply Machine Learning techniques. one way to enrich the Google Hacking Database with attributes is with Natural Language Processing and the transformation of textual values ​​to numeric, converting Dorks characters to ASCII. So, the objective was to apply Natural Language Processing to enrich Google Hacking Database with attributes and convert its textual values ​​to ASCII, to enable the application of Machine Learning techniques. The computational experiments were conducted in seven steps: Selection of the GHDB Base, Removal of Hyperlinks and Deletion of Attributes, Removal of the Site Parameter from Dorks, Removal of Outliers and Stopwords, Enrichment with Natural Language Processing, Base Transformation, and Application of the SOM. The results obtained with the application of the SOM were considered good, depending on the values ​​presented by the metrics that evaluated the network. Thus, it is considered that the objective of this paper was achieved.