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Adaptive Anomaly Detection in Performance Metric Streams

Olumuyiwa Ibidunmoye, Ali-Reza Rezaie, Erik Elmroth
2018 IEEE Transactions on Network and Service Management  
Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.  ...  Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience.  ...  Adaptive Anomaly Detection in Performance Metric Streams Olumuyiwa Ibidunmoye, Ali-Reza Rezaie, and Erik Elmroth Abstract-Continuous detection of performance anomalies such as service degradations has  ... 
doi:10.1109/tnsm.2017.2750906 fatcat:wxcrpgjm6nf2xevhfcgzxzqcqm

Anomaly Detection with HTM [chapter]

Kjell Jørgen Hole
2016 Anti-fragile ICT Systems  
The example in Fig. 12 .2 illustrates that Grok can detect anomalies that are hard for a human to see in a raw metric stream.  ...  In 2015, Numenta published source code and test data to compare the performance of anomaly detection algorithms.  ... 
doi:10.1007/978-3-319-30070-2_12 fatcat:7cpohkrydjhxxduhqroejg63ne

Anomalies Detection Using Isolation in Concept-Drifting Data Streams

Maurras Ulbricht Togbe, Yousra Chabchoub, Aliou Boly, Mariam Barry, Raja Chiky, Maroua Bahri
2021 Computers  
ADWIN is an adaptive sliding window algorithm for detecting change in a data stream.  ...  We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams.  ...  In the literature, several works have been performed for adapting or designing for ADiDS. They generally use the data stream concept of window in order to determine anomaly [25] .  ... 
doi:10.3390/computers10010013 fatcat:3t7wiggzhzbevds2k2ucvi3moe

Real-Time Anomaly Detection for Streaming Analytics [article]

Subutai Ahmad, Scott Purdy
2016 arXiv   pre-print
We show results from a live application that detects anomalies in financial metrics in real-time.  ...  Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions.  ...  Continuous learning is essential for performing anomaly detection on streaming data like this.  ... 
arXiv:1607.02480v1 fatcat:ift42qbgmvdabe7c6iwfjkeik4

Federated Anomaly Detection over Distributed Data Streams [article]

Paula Raissa Silva, João Vinagre, João Gama
2022 arXiv   pre-print
The overarching goal of the work is to detect anomalies in a federated environment over distributed data streams.  ...  This work complements the state-of-the-art by adapting the data stream algorithms in a federated learning setting for anomaly detection and by delivering a robust framework and demonstrating the practical  ...  forfraud detection in phonecalls. [5] Budget online learning algorithm to detect anomalies in imbalanced data streams Federated learning for anomaly detection [6] Deep neural network for anomalydetection  ... 
arXiv:2205.07829v2 fatcat:hjvw6su3lfhjxhvkfw4ww52ehm

Adaptive system anomaly prediction for large-scale hosting infrastructures

Yongmin Tan, Xiaohui Gu, Haixun Wang
2010 Proceeding of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing - PODC '10  
In contrast to traditional anomaly detection schemes, ALERT aims at raising advance anomaly alerts to achieve just-in-time anomaly prevention.  ...  In this paper, we present a novel adaptive runtime anomaly prediction system, called ALERT, to achieve robust hosting infrastructures.  ...  This work was sponsored in part by NSF CNS-09-1-5567, NSF CNS-09-1-5861, U.S.  ... 
doi:10.1145/1835698.1835741 dblp:conf/podc/TanGW10 fatcat:npjgl5vedbc2hm5n3zaitqnp34

Differentiated Service Protection of Multimedia Transmission via Detection of Traffic Anomalies

Hongli Luo, Mei-Ling Shyu
2007 Multimedia and Expo, 2007 IEEE International Conference on  
In this paper, we present a differentiated service protection framework consisting of anomaly traffic detection and resource management.  ...  Data mining based anomaly traffic detection is implemented at the host; whereas resource management is responsible for the allocation of network resources to the applications.  ...  Performance evaluation In the simulation, we examine how the interaction between the anomaly traffic detection and adaptive transmission management components can effectively protect QoS for the legitimate  ... 
doi:10.1109/icme.2007.4284956 dblp:conf/icmcs/LuoS07 fatcat:yypmhuz4fne5rktfht5pekiohe

Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey

Asmah Muallem, Sachin Shetty, Jan Wei Pan, Juan Zhao, Biswajit Biswal
2017 Journal of Information Security  
This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets.  ...  The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets.  ...  Acknowledgements The research presented in this paper was supported by the Office of the Assistant Secretary of Defense for Research and Engineering (OASD (R&E)) agreement FA8750-15-2-0120 and Boeing Data  ... 
doi:10.4236/jis.2017.84022 fatcat:gbpcrjc2lrdldothg4md7uwgzi

Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach [chapter]

Pieter Bonte, Sander Vanden Hautte, Annelies Lejon, Veerle Ledoux, Filip De Turck, Sofie Van Hoecke, Femke Ongenae
2020 Communications in Computer and Information Science  
Anomaly Detection (AD) allows to detect deviant behavior in these system metrics.  ...  We show that AE can be applied, without domain knowledge or manual effort and evaluate different types of AE architectures and how they perform on a variety of anomaly types found in communication networks  ...  Adaptable: as trends and seasonality in data might change, the AD should be able to adapt in order to reduce the number of false positives. 7.  ... 
doi:10.1007/978-3-030-66770-2_12 fatcat:l2mqckhsw5gkbnrnkwqwjrnzsa

A Framework for Network Intrusion Detection using Network Programmability and Data Stream Clustering Machine Learning Algorithms

Admilson de Ribamar Lima Ribeiro, Edward David Moreno Ordonez, Anderson Clayton Alves Nascimento
2019 Communication Papers of the 2019 Federated Conference on Computer Science and Information Systems  
Thus, to overcome that problem, in this paper we present an anomaly-based framework that uses network programmability and machine learning algorithms over continuous data stream.  ...  Several operational security mechanisms have been developed to mitigate malicious activity in the Internet.  ...  Thus, it is possible evaluate the performance of an anomaly detector through relating to two performance metrics. B.  ... 
doi:10.15439/2019f87 dblp:conf/fedcsis/RibeiroON19 fatcat:h2mxp46k2fgxjhtrh6wfkcltui

Anomaly Detection for Symbolic Time Series Representations of Reduced Dimensionality

Konstantinos Bountrogiannis, George Tzagkarakis, Panagiotis Tsakalides
2020 Zenodo  
However, many anomaly detection methods are unsuitable in practical scenarios, where streaming data of large volume arrive in nearly real-time at devices with limited resources.  ...  In this paper, we propose a computationally efficient, yet highly accurate, framework for anomaly detection of streaming data in lower-dimensional spaces, utilizing a modification of the symbolic aggregate  ...  On the other hand, the accuracy of anomaly detection in streaming data, which do not form predefined batches, cannot be evaluated directly with the above metrics.  ... 
doi:10.5281/zenodo.4294535 fatcat:xgeuh4zx7fe5rnwpwjxjoyig24

No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection [article]

Ece Calikus, Slawomir Nowaczyk, Anita Sant'Anna, Onur Dikmen
2020 arXiv   pre-print
In recent years, there has been increased research interest in detecting anomalies in temporal streaming data.  ...  In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection  ...  The second metric that we use in this study is NAB scoring which is a measure provided by NAB to assess the quality of streaming anomaly detection algorithms.  ... 
arXiv:1909.06927v3 fatcat:mcdk6n2mczawfgcvls23hk753q

TRACK-Plus: Optimizing Artificial Neural Networks for Hybrid Anomaly Detection in Data Streaming Systems

Ahmad S Alnafessah, Giuliano Casale
2020 IEEE Access  
To address this challenge, we introduce TRACK-Plus a black-box training methodology for performance anomaly detection.  ...  There is thus a need for effective automated performance anomaly detection methods that can be used within production environments to avoid any late detection of unexpected degradations of service level  ...  Therefore, it is essential to keep in mind that the LOF needs to be adapted to be used with Spark Streaming for anomaly detection.  ... 
doi:10.1109/access.2020.3015346 fatcat:eozee43rdbdd3fokerxz7abw6i

Towards Machine Learning-based Anomaly Detection on Time-Series Data

Daniel Vajda, Adrian Pekar, Karoly Farkas
2021 Infocommunications journal  
In this paper, we focus on a particular application of telemetry — anomaly detection on time-series data. We rigorously examined state-of-the-art anomaly detection methods.  ...  The concept of telemetry has been introduced in recent years to foster this process by streaming time-series data that contain feature-rich information concerning the state of network components.  ...  ACKNOWLEDGMENT The research reported in this paper and carried out at the BME has been supported by the NRDI Fund based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry  ... 
doi:10.36244/icj.2021.1.5 fatcat:rehnpep6sbbq3mnaecj45phmza

Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching [article]

Zhuangbin Chen, Jinyang Liu, Yuxin Su, Hongyu Zhang, Xiao Ling, Yongqiang Yang, Michael R. Lyu
2022 arXiv   pre-print
To address these limitations, in this paper, we propose ADSketch, an interpretable and adaptive performance anomaly detection approach based on pattern sketching.  ...  When performing anomaly detection over the metrics, existing methods often lack the merit of interpretability, which is vital for engineers and analysts to take remediation actions.  ...  The algorithm of adaptive pattern learning is presented in Algorithm 3, which automatically updates metric patterns during streaming anomaly detection.  ... 
arXiv:2201.02944v1 fatcat:ivhx3zrf3bfdtfxfr2xlxtnzbu
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