Cover Image

An AutoML-based approach for Network Intrusion Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gyimah, Nana Kankam, Mwakalonge, Judith, Comert, Gurcan, Siuhi, Saidi, Akinie, Robert, Sulle, Methusela, Ruganuza, Denis, Izison, Benibo, Mukwaya, Arthur
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI
by: Sulle, Methusela, et al.
Published: (2025)

Harnessing ADAS for Pedestrian Safety: A Data-Driven Exploration of Fatality Reduction
by: Sulle, Methusela, et al.
Published: (2025)

Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application
by: Sulle, Methusela, et al.
Published: (2025)

A Prescriptive Framework for Determining Optimal Days for Short-Term Traffic Counts
by: Mukwaya, Arthur, et al.
Published: (2025)

Mode Choice Heterogeneity Among Zero-Vehicle Households: A Latent Class Cluster Approach
by: Kasamala, Nancy, et al.
Published: (2025)

Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
by: Musau, Hannah, et al.
Published: (2025)

Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
by: Akinie, Robert, et al.
Published: (2025)

Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
by: Yang, Li, et al.
Published: (2024)

AutoIntent: AutoML for Text Classification
by: Alekseev, Ilya, et al.
Published: (2025)

Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models
by: Yocam, Eric, et al.
Published: (2026)

Crash Severity Risk Modeling Strategies under Data Imbalance
by: Mamun, Abdullah Al, et al.
Published: (2024)

Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System
by: Yang, Li, et al.
Published: (2025)

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
by: Trirat, Patara, et al.
Published: (2024)

ML2B: Multi-Lingual ML Benchmark For AutoML
by: Trofimova, Ekaterina, et al.
Published: (2025)

Problem-oriented AutoML in Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2024)

AutoML in Cybersecurity: An Empirical Study
by: Saad, Sherif, et al.
Published: (2025)

The Potential of AutoML for Recommender Systems
by: Vente, Tobias, et al.
Published: (2024)

AutoML Systems For Medical Imaging
by: Jidney, Tasmia Tahmida, et al.
Published: (2023)

Real-Time Conflict Prediction for Large Truck Merging in Mixed Traffic at Work Zone Lane Closures
by: Enan, Abyad, et al.
Published: (2025)

Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles
by: Majumder, Reek, et al.
Published: (2024)

AutoML in The Wild: Obstacles, Workarounds, and Expectations
by: Sun, Yuan, et al.
Published: (2023)

Robustness of AutoML on Dirty Categorical Data
by: Bueno, Marcos L. P., et al.
Published: (2026)

X Hacking: The Threat of Misguided AutoML
by: Sharma, Rahul, et al.
Published: (2024)

DREAM: Debugging and Repairing AutoML Pipelines
by: Zhang, Xiaoyu, et al.
Published: (2023)

ZeroML: A Next Generation AutoML Language
by: Mahmud, Monirul Islam
Published: (2025)

OpenAutoNLU: Open Source AutoML Library for NLU
by: Arshinov, Grigory, et al.
Published: (2026)

Exploring the impact of fairness-aware criteria in AutoML
by: Simões, Joana, et al.
Published: (2026)

NNGPT: Rethinking AutoML with Large Language Models
by: Kochnev, Roman, et al.
Published: (2025)

Budget-aware Query Tuning: An AutoML Perspective
by: Wu, Wentao, et al.
Published: (2024)

Optimal Pricing for Data-Augmented AutoML Marketplaces
by: Han, Minbiao, et al.
Published: (2023)

Hyperparameter Importance Analysis for Multi-Objective AutoML
by: Theodorakopoulos, Daphne, et al.
Published: (2024)

Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification
by: Majumder, Reek, et al.
Published: (2025)

AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
by: Tang, Zhiqiang, et al.
Published: (2024)

Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
by: da Silva, Matheus Camilo, et al.
Published: (2026)

Explainable AutoML (xAutoML) with adaptive modeling for yield enhancement in semiconductor smart manufacturing
by: Zhai, Weihong, et al.
Published: (2024)

In-Context Decision Making for Optimizing Complex AutoML Pipelines
by: Balef, Amir Rezaei, et al.
Published: (2025)

LEMUR Neural Network Dataset: Towards Seamless AutoML
by: Goodarzi, Arash Torabi, et al.
Published: (2025)

Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making
by: Kozak, Anna, et al.
Published: (2024)

AutoML Benchmark with shorter time constraints and early stopping
by: Jurado, Israel Campero, et al.
Published: (2025)

Extreme AutoML: Analysis of Classification, Regression, and NLP Performance
by: Ratner, Edward, et al.
Published: (2024)