By Associate Professor Gustavo Batista

Preprints

Azizi S; Okui N; Nakahara M; Kubota A; Batista G; Gharakaheili HH, 2026, Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling, http://dx.doi.org/10.48550/arxiv.2608.15465

Zhang Y; Batista G; Kanhere SS, 2026, Label Shift Estimation With Incremental Prior Update, http://dx.doi.org/10.48550/arxiv.2604.01651

Sivanathan A; Warren D; Mishra D; Ruj S; Fernandes N; Sheng QZ; Tran M; Luo B; Coscia D; Batista G; Gharakaheili HH, 2026, Generalizable IoT Traffic Representations for Cross-Network Device Identification, http://dx.doi.org/10.48550/arxiv.2601.19315

Zhang Y; Batista G; Kanhere SS, 2025, Instance-Wise Monotonic Calibration by Constrained Transformation, http://dx.doi.org/10.48550/arxiv.2507.06516

Nandi M; Shaghaghi A; Sultan NH; Batista G; Zhao RK; Jha S, 2025, Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI, http://dx.doi.org/10.48550/arxiv.2505.10942

Babaria RJ; Lyu M; Batista G; Sivaraman V, 2025, FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets, http://dx.doi.org/10.48550/arxiv.2504.02174

Zhang Y; Batista G; Kanhere SS, 2025, Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification, http://dx.doi.org/10.48550/arxiv.2503.20264

A CAR; Chen X; Shaghaghi A; Batista G; Kanhere S, 2025, Predicting IoT Device Vulnerability Fix Times with Survival and Failure Time Models, http://dx.doi.org/10.48550/arxiv.2501.02520

Sivanathan A; Mishra D; Ruj S; Fernandes N; Sheng QZ; Tran M; Luo B; Coscia D; Batista G; Gharakheili HH, 2024, Decoding Behavioral Patterns and Sequence Dynamics in IoT Traffic with Deep Learning, http://dx.doi.org/10.36227/techrxiv.173202912.23807435/v1

A. CAR; Shaghaghi A; Batista G; Kanhere SS, 2024, Towards Weaknesses and Attack Patterns Prediction for IoT Devices, http://dx.doi.org/10.48550/arxiv.2408.13172

Maroof U; Batista G; Shaghaghi A; Jha S, 2024, Towards Detecting IoT Event Spoofing Attacks Using Time-Series Classification, http://dx.doi.org/10.48550/arxiv.2407.19662

Hamza A; Gharakheili HH; Benson TA; Batista G; Sivaraman V, 2023, Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity, http://dx.doi.org/10.48550/arxiv.2304.04987

Pashamokhtari A; Okui N; Nakahara M; Kubota A; Batista G; Gharakheili HH, 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

Pashamokhtari A; Batista G; Gharakheili HH, 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

da Silva LT; Souza VMA; Batista GEAPA, 2021, An Open-Source Tool for Classification Models in Resource-Constrained Hardware, http://dx.doi.org/10.48550/arxiv.2105.05983

Souza VMA; Reis DMD; Maletzke AG; Batista GEAPA, 2020, Challenges in Benchmarking Stream Learning Algorithms with Real-world Data, http://dx.doi.org/10.48550/arxiv.2005.00113

Reis DD; de Souto M; de Sousa E; Batista G, 2020, Quantifying With Only Positive Training Data

Chen Y; Why A; Batista G; Mafra-Neto A; Keogh E, 2014, Flying Insect Classification with Inexpensive Sensors, http://dx.doi.org/10.48550/arxiv.1403.2654

Ebrahimi Z; Batista G; Deghat M, Aa-Dladmm: An Accelerated Admm-Based Framework for Training Deep Neural Networks, http://dx.doi.org/10.2139/ssrn.4390626

Jiang VW; Batista G; Bain M, From Fair Graphs to Fair Data: A DAG-Based Approach to Mitigating Bias in AI Systems, http://dx.doi.org/10.21203/rs.3.rs-6832455/v1

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