scholarly journals Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events

2021 ◽  
pp. 193-205
Author(s):  
Jonghyeon Ko ◽  
Marco Comuzzi
Keyword(s):  
Anomaly Detection ◽  
Business Process ◽  
Online Anomaly Detection

scholarly journals Leveraging Anomaly Detection in Business Process with Data Stream Mining

2019 ◽  
Vol 12 (1) ◽  
pp. 54-75 ◽  
Author(s):  
Gabriel Marques Tavares ◽  
Victor Guilherme Turrisi da Costa ◽  
Vinicius Eiji Martins ◽  
Paolo Ceravolo ◽  
Sylvio Barbon Jr.
Keyword(s):  
Anomaly Detection ◽  
Business Process ◽  
Data Stream ◽  
Business Processes ◽  
Performance Metrics ◽  
Real Life ◽  
Data Stream Mining ◽  
Density Based Clustering ◽  
Business Process Monitoring ◽  
Online Anomaly Detection

Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nonetheless, the continuous nature of business activities conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. Exploring different combinations of parameters, we obtained promising performance metrics, showing that our method is capable of finding anomalous process instances in a vast complexity of scenarios.

Anomaly Detection via Mining Numerical Workflow Relations from Logs

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongyu Zhang ◽  
Pablo Moscato
Keyword(s):  
Distributed Systems ◽  
Anomaly Detection ◽  
Text Messages ◽  
Distributed File System ◽  
Log Data ◽  
Sliding Windows ◽  
Novel Approach ◽  
Hadoop Distributed File System ◽  
Blue Gene ◽  
Online Anomaly Detection

<div>Complex software intensive systems, especially distributed systems, generate logs for troubleshooting. The logs are text messages recording system events, which can help engineers determine the system's runtime status. This paper proposes a novel approach named ADR (stands for Anomaly Detection by workflow Relations) that employs matrix nullspace to mine numerical relations from log data. The mined relations can be used for both offline and online anomaly detection and facilitate fault diagnosis. We have evaluated ADR on log data collected from two distributed systems, HDFS (Hadoop Distributed File System) and BGL (IBM Blue Gene/L supercomputers system). ADR successfully mined 87 and 669 numerical relations from the logs and used them to detect anomalies with high precision and recall. For online anomaly detection, ADR employs PSO (Particle Swarm Optimization) to find the optimal sliding windows' size and achieves fast anomaly detection.</div><div>The experimental results confirm that ADR is effective for both offline and online anomaly detection. </div>

scholarly journals A flexible framework for anomaly Detection via dimensionality reduction

2021 ◽  
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A. Bassett ◽  
M. Kunz
Keyword(s):  
Anomaly Detection ◽  
Dimensionality Reduction ◽  
Dimensional Space ◽  
High Dimensions ◽  
Detection Algorithms ◽  
Latent Space ◽  
Wide Range ◽  
Flexible Framework ◽  
Online Anomaly Detection ◽  
Python Package

AbstractAnomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.

Unsupervised Online Anomaly Detection to Identify Cyber-Attacks on Internet Connected Photovoltaic System Inverters

2021 ◽  
Author(s):  
C. Birk Jones ◽  
Adrian Chavez ◽  
Shamina Hossain-McKenzie ◽  
Nicholas Jacobs ◽  
Adam Summers ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Cyber Attacks ◽  
Photovoltaic System ◽  
Online Anomaly Detection

Anomaly Detection in Business Process based on Data Stream Mining

2018 ◽  
Author(s):  
Gabriel Marques Tavares ◽  
Victor G. Turrisi da Costa ◽  
Vinicius Eiji Martins ◽  
Paolo Ceravolo ◽  
Sylvio Barbon
Keyword(s):  
Anomaly Detection ◽  
Business Process ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining

scholarly journals Online Anomaly Detection of Distillation Tower System Using Adaptive Resonance Theory

2017 ◽  
Vol 50 (6) ◽  
pp. 430-438 ◽  
Author(s):  
Yoshinari Hori ◽  
Hiroki Yamamoto ◽  
Tomoko Suzuki ◽  
Jun Okitsu ◽  
Tomohiro Nakamura ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Adaptive Resonance Theory ◽  
Distillation Tower ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Online Anomaly Detection ◽  
Tower System

AECID: A Light-Weight Log Analysis Approach for Online Anomaly Detection

2021 ◽  
pp. 99-129
Author(s):  
Florian Skopik ◽  
Markus Wurzenberger ◽  
Max Landauer
Keyword(s):  
Anomaly Detection ◽  
Analysis Approach ◽  
Log Analysis ◽  
Light Weight ◽  
Online Anomaly Detection
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