Anomaly-based network intrusion detection methods

The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks

ADVANCES IN KNOWLEDGE DISCOVERY IN DATABASES

The Knowledge Discovery in Databases and Data Mining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. While Data Mining represents a set of specific algorithms of finding useful meanings in stored data, Knowledge Discovery in Databases represents the overall process of finding knowledge and includes the Data Mining as one step among others such as selection, pre�processing, transformation and interpretation of mined data. This paper aims to point the most important steps that were made in the Knowledge Discovery in Databases field of study and to show how the overall process of discovering can be improved in the future.

Machine learning classifiers: Evaluation of the performance in online reviews

This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term “sentiment value” in this study is referring to the polarity (positive, negative or neutral) of the text. This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was measured by examining overall accuracy, recall, precision, kappa statistic and applying few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. This paper explores the performance of machine learning classifiers in sentiment value classification in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers

AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis