Benchmarking Anomaly Detection Algorithms: Deep Learning and Beyond
Department of CS Don Bosco College of Engineering Goa, India（印度果阿大学计算机科学系Don Bosco学院工程学院）；Department of Computer Science and Information Systems (CSIS), BITS Pilani K. K. Birla Goa Campus, Goa, India（印度果阿BITS Pilani K. K. Birla校区计算机科学与信息系统系）
Detection of anomalous situations for complex mission-critical systems hold paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array of Machine Learning (ML)-based anomaly detection algorithms through a comprehensive benchmark study. The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical ML, including various tree-based approaches to Deep Learning (DL) and outlier detection methods. The inclusion of 104 publicly available enhances the diversity of the study, allowing a more realistic evaluation of algorithm performance and emphasizing the importance of adaptability to real-world scenarios.
The paper evaluates the general notion of DL as a universal solution, showing that, while powerful, it is not always the best fit for every scenario. The findings reveal that recently proposed tree-based evolutionary algorithms match DL methods and sometimes outperform them in many instances of univariate data where the size of the data is small and number of anomalies are less than 10%. Specifically, tree-based approaches successfully detect singleton anomalies in datasets where DL falls short. To the best of the authors' knowledge, such a study on a large number of state-of-the-art algorithms using diverse data sets, with the objective of guiding researchers and practitioners in making informed algorithmic choices, has not been attempted earlier.
Why Ranking Anomaly Detection Algorithms Isn't as Reliable as You May Think
Carnegie Mellon University（卡内基梅隆大学）；TU Dortmund University（多特蒙德工业大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；iits Consulting GmbH（iits咨询有限公司）；Fraunhofer Institute for Material Flow and Logistics IML（弗劳恩霍夫物流与材料流动研究所IML）
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；China Mobile (Jiangxi) Virtual Reality Technology Co., Ltd.（中国移动（江西）虚拟现实技术有限公司）；Salesforce AI Research（Salesforce AI研究）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Beyond a Single Perspective: Text Anomaly Detection with Multi-View Language Representations
School of Information and Communication Technology, Griffith University, Australia（信息与通信技术学院，格里菲斯大学，澳大利亚）；School of Computer, Electronics and Information, Guangxi University, China（计算机、电子与信息学院，广西大学，中国）
Domain Adaptive and Fine-grained Anomaly Detection for Single-cell Sequencing Data and Beyond
School of Statistics and Mathematics, Zhongnan University of Economics and Law（中南财经政法大学统计与数学学院）；School of Information Engineering, Zhongnan University of Economics and Law（中南财经政法大学信息工程学院）；iWudao Tech（悟道科技）
Unravelling physics beyond the standard model with classical and quantum anomaly detection
IBM Research（IBM研究院）；ETH Zürich（苏黎世联邦理工学院）；Autonomous University of Barcelona（巴塞罗那自治大学）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织（CERN））；PASQAL SAS（PASQAL SAS公司）
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
Josef Ressel Centre for Intelligent and Secure Industrial Automation（乔塞夫·雷斯尔智能与安全工业自动化中心）；Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Department of Artificial Intelligence and Human Interfaces（人工智能与人机接口部门）；Paris Lodron University of Salzburg（萨尔茨堡巴黎洛登大学）
SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification
Data and Web Science Group, University of Mannheim（曼海姆大学数据与网络科学小组）；Max-Planck-Institute for Informatics, Saarland Informatics Campus（马克斯·普朗克信息研究所，萨尔兰信息校园）
Cornell University（康奈尔大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Science and Technology of China（中国科学技术大学）；University of Sydney（悉尼大学）；Huazhong University of Science and Technology（华中科技大学）；Microsoft Research（微软研究院）；Texas A&M University（得克萨斯农工大学）
Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking
Department of Statistics, Ludwig-Maximilians-Universit \"a t M\"unchen (LMU Munich)；School of Computing \& Communications, Lancaster University Leipzig, Leipzig, Germany；CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
