scholarly journals A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

2016 ◽  
Vol 10 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Sergio Trilles ◽  
Òscar Belmonte ◽  
Sven Schade ◽  
Joaquìn Huerta
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Environmental Data ◽  
Proof Of Concept ◽  
Domain Independent

Real-Time Anomaly Detection from Environmental Data Streams

2015 ◽  
pp. 125-144 ◽  
Author(s):  
Sergio Trilles ◽  
Sven Schade ◽  
Óscar Belmonte ◽  
Joaquín Huerta
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Environmental Data

scholarly journals Real Time Anomaly Detection in Massive Data Streams with ELK Stack

2019 ◽  
Vol 15 (6) ◽  
pp. 814-823
Author(s):  
Jakup Fondaj ◽  
Zirije Hasani
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Massive Data

Detecting Insider Threats Using RADISH: A System for Real-Time Anomaly Detection in Heterogeneous Data Streams

2017 ◽  
Vol 11 (2) ◽  
pp. 471-482 ◽  
Author(s):  
Brock Bose ◽  
Bhargav Avasarala ◽  
Srikanta Tirthapura ◽  
Yung-Yu Chung ◽  
Donald Steiner
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Heterogeneous Data ◽  
Insider Threats

scholarly journals EADetection: An efficient and accurate sequential behavior anomaly detection approach over data streams

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880330 ◽  
Author(s):  
Li Cheng ◽  
Yijie Wang ◽  
Yong Zhou ◽  
Xingkong Ma
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Detection Efficiency ◽  
Detection System ◽  
False Positive Rate ◽  
Time Interval ◽  
Sequential Behavior ◽  
Detection Approach ◽  
Anomaly Score

Due to the increasing arriving rate and complex relationship of behavior data streams, how to detect sequential behavior anomaly in an efficient and accurate manner has become an emerging challenge. However, most of the existing literature simply calculates the anomaly score for segmented sequence, and there is limited work going deep to investigate data stream segment and structural relationship. Moreover, existing studies cannot meet efficiency requirements because of large number of projected subsequences. In this article, we propose EADetection, an efficient and accurate sequential behavior anomaly detection approach over data streams. EADetection adopts time interval and fuzzy logic–based correlation to segment event stream adaptively based on rolling window. Through dynamic projection space–based fast pruning, large number of repeated patterns are reduced to improve detection efficiency. Meanwhile, EADetection calculates the anomaly score by top-k pattern–based abnormal scoring based on directed loop graph–based storage strategy, which ensures the accuracy of detection. Specially, we design and implement a streaming anomaly detection system based on EADetection to perform real-time detection. Extensive experiments confirm that EADetection can achieve real time and improve accuracy, significantly reduces latency by 36.8% and reduces false positive rate by 6.4% compared with existing approach.

Real-time Bayesian anomaly detection in streaming environmental data

2009 ◽  
Vol 45 (4) ◽  
Author(s):  
David J. Hill ◽  
Barbara S. Minsker ◽  
Eyal Amir
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Environmental Data
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