Corpus ID: 210152524

Anomaly Detection for Cybersecurity: Time Series Forecasting and Deep Learning

  title={Anomaly Detection for Cybersecurity: Time Series Forecasting and Deep Learning},
  author={G. Col{\`o}},
  • G. Colò
  • Published 2020
  • Computer Science
  • viXra
Finding anomalies when dealing with a great amount of data creates issues related to the heterogeneity of different values and to the difficulty of modelling trend data during time. In this paper we combine the classical methods of time series analysis with deep learning techniques, with the aim to improve the forecast when facing time series with long-term dependencies. Starting with forecasting methods and comparing the expected values with the observed ones, we will find anomalies in time… Expand


LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
This work proposes a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. Expand
A Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Recurrent Neural Networks, the nonparametricExpand
GluonTS: Probabilistic Time Series Models in Python
Gluon Time Series is introduced, a library for deep-learning-based time series modeling that provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy. Expand
LSTM: A Search Space Odyssey
This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment. Expand
Introduction to time series and forecasting
Preface 1 INTRODUCTION 1.1 Examples of Time Series 1.2 Objectives of Time Series Analysis 1.3 Some Simple Time Series Models 1.3.3 A General Approach to Time Series Modelling 1.4 Stationary ModelsExpand
A multimodal execution monitor with anomaly classification for robot-assisted feeding
This work introduces a multimodal execution monitor to detect and classify anomalous executions when robots operate near humans and implemented and evaluated it in the context of robot-assisted feeding with a general-purpose mobile manipulator. Expand
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
A Machine Learning practitioner seeking guidance for implementing the new augmented LSTM model in software for experimentation and research will find the insights and derivations in this treatise valuable as well. Expand