ORCID as entered in ROS

Select Publications
2025, FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets, http://dx.doi.org/10.1145/3727115
,2025, Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification, http://dx.doi.org/10.48550/arxiv.2503.20264
,2025, Predicting IoT Device Vulnerability Fix Times with Survival and Failure Time Models, http://dx.doi.org/10.48550/arxiv.2501.02520
,2024, Towards Weaknesses and Attack Patterns Prediction for IoT Devices, http://dx.doi.org/10.48550/arxiv.2408.13172
,2024, Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification, http://dx.doi.org/10.48550/arxiv.2407.19662
,2023, Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity, http://dx.doi.org/10.48550/arxiv.2304.04987
,2023, Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks, http://dx.doi.org/10.48550/arxiv.2301.06695
,2022, AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks, http://dx.doi.org/10.48550/arxiv.2203.09792
,2021, An Open-Source Tool for Classification Models in Resource-Constrained Hardware, http://dx.doi.org/10.48550/arxiv.2105.05983
,2020, Challenges in Benchmarking Stream Learning Algorithms with Real-world Data, http://dx.doi.org/10.1007/s10618-020-00698-5
,2020, Quantifying With Only Positive Training Data
,2014, Flying Insect Classification with Inexpensive Sensors, http://dx.doi.org/10.48550/arxiv.1403.2654
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