{"total":28,"rows":[{"id":"2105.13289","title":"MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles","created":"2021-05-26","cited_by_count":36,"area":null},{"id":"2109.05013","title":"PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams","created":"2021-09-10","cited_by_count":17,"area":null},{"id":"2209.08018","title":"IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective","created":"2022-09-16","cited_by_count":16,"area":null},{"id":"2201.11812","title":"A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles","created":"2022-01-27","cited_by_count":11,"area":null},{"id":"2109.06088","title":"Concept Drift Detection in Federated Networked Systems","created":"2021-09-13","cited_by_count":10,"area":null},{"id":"2211.12578","title":"Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift","created":"2022-11-22","cited_by_count":10,"area":null},{"id":"2107.11514","title":"Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection","created":"2021-07-24","cited_by_count":9,"area":null},{"id":"2208.03399","title":"LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles","created":"2022-08-05","cited_by_count":6,"area":null},{"id":"2502.21286","title":"Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis","created":"2025-02-28","cited_by_count":5,"area":null},{"id":"2306.03887","title":"Fast Context Adaptation in Cost-Aware Continual Learning","created":"2023-06-06","cited_by_count":2,"area":null},{"id":"2501.15365","title":"A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data","created":"2025-01-26","cited_by_count":2,"area":null},{"id":"2502.20627","title":"Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks","created":"2025-02-28","cited_by_count":2,"area":null},{"id":"2505.24149","title":"RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2209.06852","title":"A Model Drift Detection and Adaptation Framework for 5G Core Networks","created":"2022-08-08","cited_by_count":1,"area":null},{"id":"2211.01315","title":"Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation","created":"2022-11-02","cited_by_count":1,"area":null},{"id":"2404.01109","title":"An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks","created":"2024-04-01","cited_by_count":1,"area":null},{"id":"2511.08491","title":"Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System","created":"2025-11-11","cited_by_count":1,"area":null},{"id":"2109.04356","title":"Assessing Machine Learning Approaches to Address IoT Sensor Drift","created":"2021-09-02","cited_by_count":0,"area":null},{"id":"2109.13828","title":"An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data","created":"2021-09-28","cited_by_count":0,"area":null},{"id":"2205.03537","title":"Anomaly Detection in Intra-Vehicle Networks","created":"2022-05-07","cited_by_count":0,"area":null},{"id":"2205.04622","title":"An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics","created":"2022-05-10","cited_by_count":0,"area":null},{"id":"2212.09637","title":"A Sequential Concept Drift Detection Method for On-Device Learning on Low-End Edge Devices","created":"2022-12-19","cited_by_count":0,"area":null},{"id":"2306.11135","title":"Modular Simulation Environment Towards OTN AI-based Solutions","created":"2023-06-19","cited_by_count":0,"area":null},{"id":"2308.09171","title":"Forensic Data Analytics for Anomaly Detection in Evolving Networks","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2506.15719","title":"Data-Driven Heat Pump Management: Combining Machine Learning with Anomaly Detection for Residential Hot Water Systems","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2511.02092","title":"Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2607.18164","title":"A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.18555","title":"Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments","created":"2026-08-19","cited_by_count":0,"area":null}],"next_cursor":null}