scholarly journals Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1466
Author(s):  
Kamil Faber ◽  
Marcin Pietron ◽  
Dominik Zurek
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Multivariate Time Series ◽  
Window Size ◽  
Learning Models ◽  
Failure Prevention ◽  
Detection Model ◽  
Repair Costs ◽  
Fully Automatic

Multivariate time series anomaly detection is a widespread problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms that automate the process of detecting anomalies are crucial in modern failure prevention systems. Therefore, many machine learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. This work shows a framework that incorporates neuroevolution methods to boost the anomaly detection scores of new and already known models. The presented approach adapts evolution strategies for evolving an ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimize the architecture and hyperparameters such as the window size, the number of layers, and the layer depths. The proposed framework shows that it is possible to boost most anomaly detection deep learning models in a reasonable time and a fully automated mode. We ran tests on the SWAT and WADI datasets. To the best of our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.

scholarly journals Multivariate Time Series Data Prediction Based on ATT-LSTM Network

2021 ◽  
Vol 11 (20) ◽  
pp. 9373
Author(s):  
Jie Ju ◽  
Fang-Ai Liu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Mutual Influence ◽  
Multivariate Time Series ◽  
The Other ◽  
Series Data ◽  
Data Sets ◽  
Learning Models ◽  
Prediction Problems

Deep learning models have been widely used in prediction problems in various scenarios and have shown excellent prediction effects. As a deep learning model, the long short-term memory neural network (LSTM) is potent in predicting time series data. However, with the advancement of technology, data collection has become more accessible, and multivariate time series data have emerged. Multivariate time series data are often characterized by a large amount of data, tight timeline, and many related sequences. Especially in real data sets, the change rules of many sequences will be affected by the changes of other sequences. The interacting factors data, mutation information, and other issues seriously impact the prediction accuracy of deep learning models when predicting this type of data. On the other hand, we can also extract the mutual influence information between different sequences and simultaneously use the extracted information as part of the model input to make the prediction results more accurate. Therefore, we propose an ATT-LSTM model. The network applies the attention mechanism (attention) to the LSTM to filter the mutual influence information in the data when predicting the multivariate time series data, which makes up for the poor ability of the network to process data. Weaknesses have greatly improved the accuracy of the network in predicting multivariate time series data. To evaluate the model’s accuracy, we compare the ATT-LSTM model with the other six models on two real multivariate time series data sets based on two evaluation indicators: Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The experimental results show that the model has an excellent performance improvement compared with the other six models, proving the model’s effectiveness in predicting multivariate time series data.

Deep learning models for forecasting aviation demand time series

2021 ◽  
Author(s):  
Andreas Kanavos ◽  
Fotios Kounelis ◽  
Lazaros Iliadis ◽  
Christos Makris
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Models

scholarly journals AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series

2021 ◽  
Author(s):  
Lin Zhang ◽  
Wenyu Zhang ◽  
Maxwell J. McNeil ◽  
Nachuan Chengwang ◽  
David S. Matteson ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Multivariate Time Series ◽  
Aurora A ◽  
Unified Framework

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Forecasting Video QoE with Deep Learning from Multivariate Time-series

2021 ◽  
pp. 1-1
Author(s):  
Hossein Ebrahimidinaki ◽  
Shervin Shirmohammadi ◽  
Emil Janulewicz ◽  
David Cote
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Multivariate Time Series
Sign in / Sign up

Export Citation Format

Share Document