Rafael D. C. Santos

Automated geospatial Web Services composition based on geodata quality requirements

Service-Oriented Architecture and Web Services technologies improve the performance of activities involved in geospatial analysis with a distributed computing architecture. However, the design of the geospatial analysis process on this platform, by combining component Web Services, presents some open issues. The automated construction of these compositions represents an important research topic. Some approaches to solving this problem are based on AI planning methods coupled with semantic service descriptions. This work presents a new approach using AI planning methods to improve the robustness of the produced geospatial Web Services composition. For this purpose, we use semantic descriptions of geospatial data quality requirements in a rule-based form. These rules allow the semantic annotation of geospatial data and, coupled with the conditional planning method, this approach represents more precisely the situations of nonconformities with geodata quality that may occur during the execution of the Web Service composition. The service compositions produced by this method are more robust, thus improving process reliability when working with a composition of chained geospatial Web Services.

STARS: A New Method for Multitemporal Remote Sensing

There is great potential for the development of remote sensing methods that integrate and exploit both multispectral and multitemporal information. This paper presents a new image processing method: Spectral-Temporal Analysis by Response Surface (STARS), which synthesizes the full information content of a multitemporal-multispectral remote sensing image data set to represent the spectral variation over time of features on the Earths surface. Depending on the application, STARS can be effectively implemented using a range of different models [e.g., polynomial trend surface (PTS) and collocation surface (CS)], exploiting data from different sensors, with varying spectral wavebands and acquiring data at irregular time intervals. A case study was used to test STARS, evaluating its potential to characterize sugarcane harvest practices in Brazil, specifically with and without preharvest straw burning. Although the CS model presented sharper and more defined spectral-temporal surfaces, abrupt changes related to the sugarcane harvest event were also well characterized with the PTS model when a suitable degree was set. Orthonormal coefficients were tested for both the PTS and CS models and performed more accurately than regular coefficients when used as input for three evaluated classifiers: instance based, decision tree, and neural network. Results show that STARS holds considerable potential for representing the spectral changes over time of features on the Earths surface, thus becoming an effective image processing method, which is useful not only for classification purposes but also for other applications such as understanding land-cover change. The STARS algorithm can be found at www.dsr.inpe.br/~mello.

Evaluation of data mining techniques for suspicious network activity classification using honeypots data

As the amount and types of remote network services increase, the analysis of their logs has become a very difficult and time consuming task. There are several ways to filter relevant information and provide a reduced log set for analysis, such as whitelisting and intrusion detection tools, but all of them require too much fine- tuning work and human expertise. Nowadays, researchers are evaluating data mining approaches for intrusion detection in network logs, using techniques such as genetic algorithms, neural networks, clustering algorithms, etc. Some of those techniques yield good results, yet requiring a very large number of attributes gathered by network traffic to detect useful information. In this work we apply and evaluate some data mining techniques (K-Nearest Neighbors, Artificial Neural Networks and Decision Trees) in a reduced number of attributes on some log data sets acquired from a real network and a honeypot, in order to classify traffic logs as normal or suspicious. The results obtained allow us to identify unlabeled logs and to describe which attributes were used for the decision. This approach provides a very reduced amount of logs to the network administrator, improving the analysis task and aiding in discovering new kinds of attacks against their networks.

Behavioral analysis of malicious code through network traffic and system call monitoring

Malicious code (malware) that spreads through the Internet-such as viruses, worms and trojans-is a major threat to information security nowadays and a profitable business for criminals. There are several approaches to analyze malware by monitoring its actions while it is running in a controlled environment, which helps to identify malicious behaviors. In this article we propose a tool to analyze malware behavior in a non-intrusive and effective way, extending the analysis possibilities to cover malware samples that bypass current approaches and also fixes some issues with these approaches.

A Web Service-based Framework for Temporal/Spatial Environmental Data Access

The Weather Forecast and Climate Studies Center (CPTEC) of the Brazilian National Institute for Space Research (INPE) is responsible for weather and climate forecasts and weather data collection and dissemination. Data is collected from several different systems and networks, with different sensors and features. Data is available through the Institutes web site through searchable web pages, which allows the easy retrieval of small amounts of information stored on the servers at a time, but only for a specific type of data (e.g. sensor). Although they serve their main purpose, these interfaces present limited usability: if an user needs to create a composite query involving different sensors or even different temporal/spatial slices of the data, this user will need to perform several queries while storing the intermediate results. In this paper we present a framework to access data and metadata stored in CPTECs server that allows composition of complex queries through web services. These web services cover several different data sources, allowing the exploration of the data in ways not considered by the institute. Some simple case studies and first results are demonstrated.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes.

PHM-based Multi-UAV task assignment

This paper is relating to the application of Integrated Vehicle Health Management (IVHM) concepts based on Prognostics and Health Monitoring (PHM) techniques to Multi-UAV systems. Considering UAV as a mission critical system, it is expected and required to accomplish its operational objectives with minimal unscheduled interruptions. So that, it does make sense for UAV to take advantage of those techniques as enablers for the readiness of multi-UAV. The main goal of this paper is to apply information from a PHM system to support decision making through an IVHM framework. PHM system information, in this case, comprises UAV remaining useful life (RUL) estimations. UAV RUL is computed by means of a fault tree analysis that it is fed by a distribution function from a probability density function relating time and failure probability for each UAV critical components. The IVHM framework, in this case, it is the task assignment based on UAV health condition (RUL information) using the Receding Horizon Task Assignment (RHTA) algorithm. The study case was developed considering a team of electrical small UAVs and pitch control system was chosen as the critical system.

Ten Years of SkyServer I: Tracking Web and SQL e-Science Usage

SkyServer is the primary catalog data portal of the Sloan Digital Sky Survey that makes multiple terabytes of astronomy data available to the world. Here, the process is described of collecting and analyzing the complete record of more than 10 years of Web hits and SQL queries to SkyServer.

Exploiting Client Logs to Support the Construction of Adaptive e-Commerce Applications

An essential feature of successful e-commerce applications is the ability to provide the right content at the right time for the user. Therefore, personalization techniques have been exploited to build adaptive applications in which the user interfaces change according to the user needs and preferences. In this work, we advocate that it is possible to achieve personalization by analyzing the behavior of the user when browsing an e-commerce application. In order to prove our hypothesis, we built a toolkit that allows the automatic gathering and analysis of client logs in real-time. Moreover, our solution provides a Web service that exposes the analysis outcome also in real-time, thus allowing the client application to adapt on-the fly according to the results provided by the toolkit. This paper presents a case study that demonstrate the effectiveness of our approach to support the construction of adaptive e-commerce applications.

Teaching-Learning Environment Tool to Promote Individualized Student Assistance

To develop an effective teaching-learning process for a group of students respecting their individual learning pace is a challenging task for teachers. To assist students individually, it is necessary to identify each student’s difficulty and take appropriate teaching action. This paper presents an assisted learning tool based on the web that monitors and reports the student’s learning behavior for the teacher. This tool, called eTutor, also performs preconfigured actions (i.e., displays a video or text) according to the current state of student learning. We tested this tool in two different topics for two groups of students. The evaluation showed that this tool promotes student assistance, helping the teachers to be closer to their students.