PAC-Wrap: Semi-Supervised PAC Anomaly Detection (original) (raw)

Deep Semi-Supervised Anomaly Detection

Robert Vandermeulen

ArXiv, 2020

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Comparative Evaluation of Semi-Supervised Anomaly Detection Algorithms on High-Integrity Digital Systems

Julien Branlard

2021 24th Euromicro Conference on Digital System Design (DSD), 2021

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Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions

Lorenzo Perini

2020

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A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms

Jean-Charles Verdier, Pierre-Martin Tardif

2022

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Semi-supervised Statistical Approach for Network Anomaly Detection

Mohamed Guerroumi

Procedia Computer Science, 2016

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Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection

Jinsung Yoon

arXiv (Cornell University), 2021

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SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch

Jinsung Yoon

arXiv (Cornell University), 2022

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A novel Approach Using “Supervised and Unsupervised learning” to prevent the Adequacy of Intrusion Detection Systems

pradeep mallick

International Journal of Engineering & Technology, 2018

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ANTIDOTE: Understanding and defending against poisoning of anomaly detectors

Benjamin Rubinstein

2009

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A Novel Warning Identification Framework for Risk-Informed Anomaly Detection

Vidar Hepsø

Journal of Intelligent & Robotic Systems

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Anomaly Detection in Autonomous Vehicle Using ML Approach

atul kathole

2021

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Training robust anomaly detection using ML-Enhanced simulations

Philip Feldman

ArXiv, 2020

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Anomaly and Novelty detection for robust semi-supervised learning

Francesca Greselin

Statistics and Computing

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A training-resistant anomaly detection system

Carlo Harpes

Computers & Security

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Recent Progress of Anomaly Detection

Huawen Liu

Complexity

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Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation

AR TH

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), 2021

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POSTER: Revisiting anomaly detection system design philosophy

Ayesha Ashfaq

Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security - CCS '13, 2013

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Supervised Hyperparameter Estimation for Anomaly Detection

Jose Dorronsoro

2020

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Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy

Apoorva Nandini Saridena

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018

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Enhancing one-class support vector machines for unsupervised anomaly detection

Mennatallah Amer

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Universal Anomaly Detection: Algorithms and Applications

Shachar Siboni, Asaf Cohen

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Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection

Marcella Astrid

ArXiv, 2021

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AnomalyAdapters: Parameter-efficient Multi-Anomaly Task Detection

Hasan Dag

IEEE Access

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Towards Total Recall in Industrial Anomaly Detection

JOAQUIN ZEPEDA

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Casting out Demons: Sanitizing Training Data for Anomaly Sensors

Gabriela Ciocarlie

2008 IEEE Symposium on Security and Privacy (sp 2008), 2008

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Conditional Anomaly Detection

Sanjay Ranka

IEEE Transactions on Knowledge and Data Engineering, 2000

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Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators

Smita Krishnaswamy

2020

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A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

Alberto Huertas Celdran

IEEE Access

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Sequential Anomaly Detection in the Presence of Noise and Limited Feedback

Jorge Silva

IEEE Transactions on Information Theory, 2012

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A Generalized Active Learning Approach for Unsupervised Anomaly Detection

Adriano Veloso

2018

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Energy Efficient Distributed Anomaly Detection using Semi-Supervised Models in IoT

IJICTR Journal

International Journal of Information and Communication Technology Research , 2024

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Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

Andrea Bondavalli

ACM/IMS Transactions on Data Science, 2021

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Rethinking Assumptions in Deep Anomaly Detection

Robert Vandermeulen

2020

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Self-Trained One-class Classification for Unsupervised Anomaly Detection

Jinsung Yoon

arXiv (Cornell University), 2021

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A Novel Framework for Incorporating Labeled Examples into Anomaly Detection

Pang-ning Tan

Proceedings of the 2006 SIAM International Conference on Data Mining, 2006

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