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MStream: Fast Anomaly Detection in Multi-Aspect Streams

Authors:

Siddharth Bhatia,

Bryan HooiAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 3371 - 3382

https://doi.org/10.1145/3442381.3450023

Published: 03 June 2021 Publication History

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Abstract

Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow us to take into account additional attributes of each entry. Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream which can detect unusual group anomalies as they occur, in a dynamic manner. MStream has the following properties: (a) it detects anomalies in multi-aspect data including both categorical and numeric attributes; (b) it is online, thus processing each record in constant time and constant memory; (c) it can capture the correlation between multiple aspects of the data. MStream is evaluated over the KDDCUP99, CICIDS-DoS, UNSW-NB 15 and CICIDS-DDoS datasets, and outperforms state-of-the-art baselines.

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WWW '21: Proceedings of the Web Conference 2021

April 2021

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ISBN:9781450383127

DOI:10.1145/3442381

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Cited By

Sun SSankararaman ANarayanaswamy BSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Online adaptive anomaly thresholding with confidence sequencesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693987(47105-47132)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693987
Niu ZXue JWang YLei THan WGao X(2024)QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic StreamsChinese Journal of Electronics10.23919/cje.2022.00.36033:3(645-656)Online publication date: May-2024
https://doi.org/10.23919/cje.2022.00.360
Zhu JCai SDeng FOoi BZhang W(2024)METER: A Dynamic Concept Adaptation Framework for Online Anomaly DetectionProceedings of the VLDB Endowment10.14778/3636218.363623317:4(794-807)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.14778/3636218.3636233
Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/366990618:8(1-44)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3669906
Li XXie KWang XXie GLi KCao JZhang DWen J(2024)A Light-Weight and Robust Tensor Convolutional Autoencoder for Anomaly DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333278436:9(4346-4360)Online publication date: Sep-2024
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Chen YZeng HXia HJia H(2024)Operations and Maintenance KPI Anomaly Detection Based on VAE-SA-BILSTM Hybrid Model2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692985(259-264)Online publication date: 22-Mar-2024
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Flood REngelen GAspinall DDesmet L(2024)Bad Design Smells in Benchmark NIDS Datasets2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP60621.2024.00042(658-675)Online publication date: 8-Jul-2024
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Escriche ENyberg JKim YDán G(2024)Channel-Centric Spatio-Temporal Graph Networks for Network-based Intrusion Detection2024 IEEE Conference on Communications and Network Security (CNS)10.1109/CNS62487.2024.10735668(1-9)Online publication date: 30-Sep-2024
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Li MFan ZSong LGuo L(2024)Research on Adaptive Model Pooling Method for Data Stream Anomaly Detection Based on Variational Auto-Encoder2024 China Automation Congress (CAC)10.1109/CAC63892.2024.10865256(4127-4131)Online publication date: 1-Nov-2024
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Kim YKim JKim D(2024)Hi-MLIC: Hierarchical Multilayer Lightweight Intrusion Classification for Various Intrusion ScenariosIEEE Access10.1109/ACCESS.2024.345067112(120098-120115)Online publication date: 2024
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