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Anomalies Detection Using Isolation in Concept-Drifting Data Streams  †

ISEP, LISITE, 75006 Paris, France
Faculté des Sciences et Techniques (FST)/Département Mathématiques et Informatique, Université Cheikh Anta Diop de Dakar, Dakar-Fann BP 5005, Senegal
Télécom Paris, LTCI, Institut Polytechnique de Paris, 91120 Palaiseau, France
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in International Conference on Computational Science and Its Applications (ICCSA 2020), Springer.
Computers 2021, 10(1), 13;
Received: 23 December 2020 / Revised: 9 January 2021 / Accepted: 12 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Selected Papers from ICCSA 2020)
Detecting anomalies in streaming data is an important issue for many application domains, such as cybersecurity, natural disasters, or bank frauds. Different approaches have been designed in order to detect anomalies: statistics-based, isolation-based, clustering-based, etc. In this paper, we present a structured survey of the existing anomaly detection methods for data streams with a deep view on Isolation Forest (iForest). We first provide an implementation of Isolation Forest Anomalies detection in Stream Data (IForestASD), a variant of iForest for data streams. This implementation is built on top of scikit-multiflow (River), which is an open source machine learning framework for data streams containing a single anomaly detection algorithm in data streams, called Streaming half-space trees. We performed experiments on different real and well known data sets in order to compare the performance of our implementation of IForestASD and half-space trees. Moreover, we extended the IForestASD algorithm to handle drifting data by proposing three algorithms that involve two main well known drift detection methods: ADWIN and KSWIN. ADWIN is an adaptive sliding window algorithm for detecting change in a data stream. KSWIN is a more recent method and it refers to the Kolmogorov–Smirnov Windowing method for concept drift detection. More precisely, we extended KSWIN to be able to deal with n-dimensional data streams. We validated and compared all of the proposed methods on both real and synthetic data sets. In particular, we evaluated the F1-score, the execution time, and the memory consumption. The experiments show that our extensions have lower resource consumption than the original version of IForestASD with a similar or better detection efficiency. View Full-Text
Keywords: anomaly detection; isolation-based; data streams; drift detection; survey anomaly detection; isolation-based; data streams; drift detection; survey
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MDPI and ACS Style

Togbe, M.U.; Chabchoub, Y.; Boly, A.; Barry, M.; Chiky, R.; Bahri, M. Anomalies Detection Using Isolation in Concept-Drifting Data Streams . Computers 2021, 10, 13.

AMA Style

Togbe MU, Chabchoub Y, Boly A, Barry M, Chiky R, Bahri M. Anomalies Detection Using Isolation in Concept-Drifting Data Streams . Computers. 2021; 10(1):13.

Chicago/Turabian Style

Togbe, Maurras U., Yousra Chabchoub, Aliou Boly, Mariam Barry, Raja Chiky, and Maroua Bahri. 2021. "Anomalies Detection Using Isolation in Concept-Drifting Data Streams " Computers 10, no. 1: 13.

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