Task-Decomposition based Anomaly Detection of Massive And High-Volatility Session Data on Academic Backbone Network

The Science Information Network (SINET) is a Japanese academic backbone network for more than 800 universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly variable. In this paper, we present a task-decomposition based anomaly detection of massive and highvolatility session data of SINET. Three main features are discussed: Tash scheduling, Traffic discrimination, and Histogramming. We adopt a task-decomposition based dynamic scheduling method to handle the massive session data stream of SINET. In the experiment, we have analysed SINET traffic from 2/27 to 3/8 and detect some anomalies by LSTM based time-series data processing.

Download Full-text

Anomaly Detection for Time Series Data Stream

2021 IEEE 6th International Conference on Big Data Analytics (ICBDA) ◽

10.1109/icbda51983.2021.9402957 ◽

2021 ◽

Author(s):

Qifan Wang ◽

Bo Yan ◽

Hongyi Su ◽

Hong Zheng

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Data Stream ◽

Time Series Data ◽

Series Data

Download Full-text

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.136.363 ◽

2016 ◽

Vol 136 (3) ◽

pp. 363-372

Author(s):

Takaaki Nakamura ◽

Makoto Imamura ◽

Masashi Tatedoko ◽

Norio Hirai

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Detection Method ◽

Series Data

Download Full-text

LRZ Convolution: An Algorithm for Automatic Anomaly Detection in Time-series Data

32nd International Conference on Scientific and Statistical Database Management ◽

10.1145/3400903.3400904 ◽

2020 ◽

Author(s):

Arunprasad P. Marathe

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Series Data

Download Full-text

Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3439870 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1-18

Author(s):

Jessamyn Dahmen ◽

Diane J. Cook

Keyword(s):

Anomaly Detection ◽

Time Series Data ◽

Bayesian Optimization ◽

Detection Methods ◽

Series Data ◽

Detection Techniques ◽

Health Events ◽

Warm Start ◽

Health Related ◽

Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

Download Full-text

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽

10.3390/w13121633 ◽

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.

Download Full-text