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Concept Drift and Anomaly Detection in Graph Streams
2018
IEEE Transactions on Neural Networks and Learning Systems
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Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes. ...
In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings ...
Concept Drift and Anomaly Detection in Graph Streams Daniele Zambon, Student Member, IEEE, Cesare Alippi, Fellow, IEEE, and Lorenzo Livi, Member, IEEE Abstract-Graph representations offer powerful and ...
doi:10.1109/tnnls.2018.2804443
pmid:29994077
fatcat:hbulbnosajgptfemcegtenwomi
The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection
2018
Symmetry
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Compared with automatic outlier detection for data streams (A-ODDS) and with sliding nest window chart anomaly detection based on data streams (SNWCAD-DS), the DCUSUM-DS can account for concept drift and ...
This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window methods. ...
The difficulty of data stream anomaly detection lies in concept drift. The future distribution of a data stream is unknown. ...
doi:10.3390/sym10070264
fatcat:4xavlltw2jdhzdmjcbdnlgtwnq
Application of Sliding Nest Window Control Chart in Data Stream Anomaly Detection
2018
Symmetry
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detection based on the data stream (SNWCAD-DS) by employing the concept of the sliding window and control chart. ...
ratio and classifies the conceptual drift data stream online. ...
The data stream anomaly detection includes concept drift [16] and drift level [17] detection, which is an online classification problem. ...
doi:10.3390/sym10040113
fatcat:e2t2ygn6dzhjjiqjirwfrv54ma
Multi Novel Class Classification of Feature Evolving Data Streams with J48
2015
International Journal of Computer Applications
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In the Data stream classification main issues are infinite length, concept drift, concept development, and feature development. ...
In the existing system of data stream method researcher tackle on the only two issues i.e. concept drift and concept evolution problem of classification. ...
Initially in [13] studied the novel class detection problem in the neighbourhood of concept-drift and infinite length. ...
doi:10.5120/ijca2015905652
fatcat:bgxakksgfjdlfcgdyf7eahodky
Introduction to the special issue on discovery science
2018
Machine Learning
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Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
The paper introduces the TORNADA framework, in which multiple diverse classifiers and drift detection algorithms are executed in parallel. ...
Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams" by Pesaranghader et al. focuses on adaptive learning from evolving data streams. ...
doi:10.1007/s10994-018-5749-6
fatcat:aisy73tcirexxnd45d5b34wyy4
An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges
2021
Symmetry
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We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. ...
This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN ...
In fuzzy-based anomaly detection, the rule-based is connected to inputs. ...
doi:10.3390/sym13061011
fatcat:zpq3ds2ifnfmxddbnhxuej7knm
EVOLVING INSIDER THREAT DETECTION STREAM MINING PERSPECTIVE
2013
International journal on artificial intelligence tools
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This paper applies ensemble-based stream mining, supervised and unsupervised learning, and graph-based anomaly detection to the problem of insider threat detection. ...
Ensemble-based stream mining leverages multiple classification models to achieve highly accurate anomaly detection in such streams, even when the stream is unbounded, evolving, and unlabeled. ...
data as a graph and apply unsupervised learning to detect anomalies. ...
doi:10.1142/s0218213013600130
fatcat:qntmuz7zrjaanl56qfaajvcrra
Anomaly, event, and fraud detection in large network datasets
2013
Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13
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Detecting anomalies and events in data is a vital task, with numerous applications in security, finance, health care, law enforcement, and many others. ...
The goal of this tutorial is to provide a general, comprehensive overview of the state-of-the-art methods for anomaly, event, and fraud detection in data represented as graphs. ...
That is, methods in this topic are grouped into outlier, anomaly, fraud, event, change, drift, fault detection separately. ...
doi:10.1145/2433396.2433496
dblp:conf/wsdm/AkogluF13
fatcat:i7m6c3g7j5agvnova3javx3aly
Guest Editorial: Non-IID Outlier Detection in Complex Contexts
2021
IEEE Intelligent Systems
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We believe this work provides an example of joint classification and outlier detection on imbalanced streaming data with potential concept drift. ...
In "Anomaly detection aided budget online classification for imbalanced data streams," 8 Liang et al. study the problem of classification on imbalanced streaming data in the presence of outliers. ...
doi:10.1109/mis.2021.3072704
fatcat:z7ohc4yue5hw7gmsauflfwklqu
Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data
2020
Information
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complexity, and response to concept drifts. ...
drift in the most effective way. ...
between run-time performance, anomaly-detection accuracy, and response to concept drifts. ...
doi:10.3390/info11060315
fatcat:ly5wmmmxpzbb7ltjp3mn65u6sq
Analyzing Performance of Classification Algorithms on Concept Drifted Data Streams
2017
International Journal of Computer Applications
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In this paper the main issue of concept drift is addressed with real and synthetic data streams and the comparison of ensemble classifiers has been made in view of concept drift for the assessment of the ...
Various classifiers were applied on data stream with and without concept drift for analysis. ...
Sudden drift in the graph shows the occurrence of concept drift in the data streams. ...
doi:10.5120/ijca2017913065
fatcat:u7yda2myobcxdjvgkxxfztcqb4
Data stream mining techniques: a review
2019
TELKOMNIKA (Telecommunication Computing Electronics and Control)
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In this paper, we review real time clustering and classification mining techniques for data stream. ...
Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. ...
KME [33] is a recent classifier leverages supervised and unsupervised knowledge to detect concept drift and recognizes recurrent concepts. ...
doi:10.12928/telkomnika.v17i2.11752
fatcat:rls2qzcl3vhobmkpycsdwhzplu
Analysis of Classification and Clustering based Novel Class Detection Techniques for Stream Data Mining
2015
International Journal of Engineering Research and
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Concept drift means data changes rapidly over time and novel class define as new class appear in continuous data stream. ...
Due to its dynamic changing nature it has some major challenges like infinite length, novel class detection and concept-drift. ...
Concept drift that means data always changes over time and concept evolution means new class arrived in data stream. ...
doi:10.17577/ijertv4is100160
fatcat:cqtpjn4qxrb4pndtteg4uhxlfu
Concept Drift Detection Based on Anomaly Analysis
[chapter]
2014
Lecture Notes in Computer Science
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In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift Detection (AADD), to improve the performance of machine learning algorithms under non-stationary ...
Experiments illustrate that this AADD method can detect new concept quickly and learn extensional drift incrementally. ...
Secondly, in Section 4.2, we show the accuracy change caused by concept drift and plot the anomaly points at each time step on a graph. ...
doi:10.1007/978-3-319-12637-1_33
fatcat:4d2zjzl5ordzzaxui5eka27ywe
Identifying Concept-drift in Twitter Streams
2015
Procedia Computer Science
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Identification and handling of this concept-drift in such Big Data Streams is present area of interest. ...
This study puts forward a novel approach towards identifying concept-drift by initially grouping topics into classes and assigning weight-age for each class, using sliding window processing model upon ...
In short, Concept-Drift Analysis is the integrated study of identifying and handling Concept-Drift in this evolving stream of data. ...
doi:10.1016/j.procs.2015.03.093
fatcat:bzaaw2z6tnhtzn4qh3sjlqmvii
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