A Demonstration of the Exathlon Benchmarking Platform for Explainable Anomaly Detection

被引：0

作者：

Jacob, Vincent ^{[1
]}

Song, Fei ^{[1
]}

Stiegler, Arnaud ^{[1
]}

Rad, Bijan ^{[1
]}

Diao, Yanlei ^{[1
]}

Tatbul, Nesime ^{[2
,3
]}

机构：

[1] Ecole Polytech, Palaiseau, France

[2] Intel Labs, New York, NY USA

[3] MIT, Cambridge, MA 02139 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2021年 / 14卷 / 12期

基金：

欧洲研究理事会;

关键词：

D O I：

10.14778/3476311.3476355

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this demo, we introduce Exathlon - a new benchmarking platform for explainable anomaly detection over high-dimensional time series. We designed Exathlon to support data scientists and researchers in developing and evaluating learned models and algorithms for detecting anomalous patterns as well as discovering their explanations. This demo will showcase Exathlon's curated anomaly dataset, novel benchmarking methodology, and end-to-end data science pipeline in action via example usage scenarios.

引用

页码：2827 / 2830

页数：4

共 50 条

[11] A Taxonomy and Platform for Anomaly Detection
Sebestyen, Gheorghe
Hangan, Anca
Czako, Zoltan
Kovacs, Gyorgy
2018 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR), 2018,
[12] Explainable Anomaly Detection for Industrial Control System Cybersecurity
Do Thu Ha
Nguyen Xuan Hoang
Nguyen Viet Hoang
Nguyen Huu Du
Truong Thu Huong
Kim Phuc Tran
IFAC PAPERSONLINE, 2022, 55 (10): : 1183 - 1188
[13] Explainable unsupervised anomaly detection for healthcare insurance data
De Meulemeester, Hannes
De Smet, Frank
van Dorst, Johan
Derroitte, Elise
De Moor, Bart
BMC MEDICAL INFORMATICS AND DECISION MAKING, 2025, 25 (01)
[14] Distributed and explainable GHSOM for anomaly detection in sensor networks
Mignone, Paolo
Corizzo, Roberto
Ceci, Michelangelo
MACHINE LEARNING, 2024, 113 (07) : 4445 - 4486
[15] A Survey on Explainable Anomaly Detection for Industrial Internet of Things
Huang, Zijie
Wu, Yulei
2022 5TH IEEE CONFERENCE ON DEPENDABLE AND SECURE COMPUTING (IEEE DSC 2022), 2022,
[16] DeepEAD: Explainable Anomaly Detection from System Logs
Wang, Xinda
Kim, Kyeong Jin
Wang, Ye
Koike-Akino, Toshiaki
Parsons, Kieran
ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 771 - 776
[17] Explainable and Interpretable Anomaly Detection Models for Production Data
Alharbi, Basma
Liang, Zhenwen
Aljindan, Jana M.
Agnia, Ammar K.
Zhang, Xiangliang
SPE JOURNAL, 2022, 27 (01): : 349 - 363
[18] GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
Tang, Jianheng
Hua, Fengrui
Gao, Ziqi
Zhao, Peilin
Li, Jia
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
[19] BENCHMARKING OF ANOMALY DETECTION ALGORITHMS ON AUTOMATED PASSWORD ATTACKS
Strnad, Pavel
Svarc, Lukas
DIGITALIZED ECONOMY, SOCIETY AND INFORMATION MANAGEMENT (IDIMT-2020), 2020, 49 : 237 - 243
[20] Explainable Anomaly Detection via Feature-Based Localization
Kitamura, Shogo
Nonaka, Yuichi
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS, 2019, 11731 : 408 - 419

← 1 2 3 4 5 →