Predictive Abuse Detection for a PLC Smart Lighting Network Based on Automatically Created Models of Exponential Smoothing

One of the basic elements of a Smart City is the urban infrastructure management system, in particular, systems of intelligent street lighting control. However, for their reliable operation, they require special care for the safety of their critical communication infrastructure. This article presents solutions for the detection of different kinds of abuses in network traffic of Smart Lighting infrastructure, realized by Power Line Communication technology. Both the structure of the examined Smart Lighting network and its elements are described. The article discusses the key security problems which have a direct impact on the correct performance of the Smart Lighting critical infrastructure. In order to detect an anomaly/attack, we proposed the usage of a statistical model to obtain forecasting intervals. Then, we calculated the value of the differences between the forecast in the estimated traffic model and its real variability so as to detect abnormal behavior (which may be symptomatic of an abuse attempt). Due to the possibility of appearance of significant fluctuations in the real network traffic, we proposed a procedure of statistical models update which is based on the criterion of interquartile spacing. The results obtained during the experiments confirmed the effectiveness of the presented misuse detection method

USING MACHINE LEARNING TO OPTIMIZE PREDICTIVE MODELS USED FOR BIG DATA ANALYTICS IN VARIOUS SPORTS EVENTS

In today’s world, data is growing in huge volume and type day by day. Historical data can hence be leveraged to predict the likelihood of the events which are to occur in the future. This process of using statistical or any other form of data to predict future outcomes is commonly termed as predictive modelling. Predictive modelling is becoming more and more important and is trending because of several reasons. But mainly, it enables businesses or individual users to gain accurate insights and allows to decide suitable actions for a profitable outcome. Machine learning techniques are generally used in order to build these predictive models. Examples of machine learning models ranges from time-series-based regression models which can be used for predicting volume of airline related traffic and linear regression-based models which can be used for predicting fuel efficiency. There are many domains which can gain competitive advantage by using predictive modelling with machine learning. Few of these domains include, but are not limited to, banking and financial services, retail, insurance, fraud detection, stock market analysis, sentimental analysis etc. In this research project, predictive analysis is used for the sports domain. It’s an upcoming domain where machine learning can help make better predictions. There are numerous sports events happening around the globe every day and the data gathered from these events can very well be used for predicting as well as improving the future events. In this project, machine learning with statistics would be used to perform quantitative and predictive analysis of dataset related to soccer. Comparisons of these models to see how effectively the models are is also presented. Also, few big data tools and techniques are used in order to optimize these predictive models and increase their accuracy to over 90%

Daphne: A tool for anomaly detection

En este trabajo se presenta una nueva herramienta dirigida a la deteccion y análisis de anomalias. Ésta permite el estudio de cualquier serie temporal, tanto de una variable, como de múltiples variables. La herramienta se compone de dos partes. Un "cerebro", en el que se han implementado las metodologías para la detección de anomalias, así como las herramientas para el análisis de las mismas. Y una interfaz, que permite la interacción con el usuario. En la memoria se detallan los algoritmos y herramientas implementadas. Para demostrar el potencial de la herramienta, se presenta también un caso práctico de aplicación.Outgoin