@article{MTMT:32184043,
	title = {SapiAgent: A Bot Based on Deep Learning to Generate Human-like Mouse Trajectories},
	url = {https://m2.mtmt.hu/api/publication/32184043},
	author = {Antal, Margit and Buza, Krisztián Antal and Fejér, Norbert},
	doi = {10.1109/ACCESS.2021.3111098},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {9},
	unique-id = {32184043},
	issn = {2169-3536},
	year = {2021},
	eissn = {2169-3536},
	pages = {124396-124408},
	orcid-numbers = {Antal, Margit/0000-0003-3596-1365}
}

@inproceedings{MTMT:32178141,
	title = {SapiMouse: Mouse Dynamics-based User Authentication Using Deep Feature Learning},
	url = {https://m2.mtmt.hu/api/publication/32178141},
	author = {Antal, Margit and Fejér, Norbert and Buza, Krisztián Antal},
	booktitle = {15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021},
	doi = {10.1109/SACI51354.2021.9465583},
	unique-id = {32178141},
	year = {2021},
	pages = {61-66}
}

@inproceedings{MTMT:32178138,
	title = {Feature Learning for Accelerometer based Gait Recognition},
	url = {https://m2.mtmt.hu/api/publication/32178138},
	author = {Nemes, Szilard and Antal, Margit},
	booktitle = {15th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2021},
	doi = {10.1109/SACI51354.2021.9465576},
	unique-id = {32178138},
	year = {2021},
	pages = {479-484}
}

@CONFERENCE{MTMT:32218548,
	title = {Egérdinamika alapú felhasználó azonosítás mély neuronhálók segítségével},
	url = {https://m2.mtmt.hu/api/publication/32218548},
	author = {Antal, Margit and Fejér, Norbert},
	booktitle = {XXI. Energetika-Elektrotechnika – ENELKO és XXX. Számítástechnika és Oktatás – SzámOkt Multi-konferencia},
	unique-id = {32218548},
	year = {2020},
	pages = {63-68}
}

@article{MTMT:31380234,
	title = {Mouse dynamics based user recognition using deep learning},
	url = {https://m2.mtmt.hu/api/publication/31380234},
	author = {Antal, Margit and Fejér, Norbert},
	journal-iso = {ACTA UNIV SAP INFORM},
	journal = {ACTA UNIVERSITATIS SAPIENTIAE INFORMATICA},
	volume = {12},
	unique-id = {31380234},
	issn = {1844-6086},
	abstract = {Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.},
	year = {2020},
	eissn = {2066-7760},
	pages = {39-50},
	orcid-numbers = {Antal, Margit/0000-0003-3596-1365}
}

@inproceedings{MTMT:31605588,
	title = {User Verification Based on Mouse Dynamics: a Comparison of Public Data Sets},
	url = {https://m2.mtmt.hu/api/publication/31605588},
	author = {Antal, Margit and Dénes-Fazakas, Lehel},
	booktitle = {SACI 2019 : IEEE 13th International Symposium on Applied Computational Intelligence and Informatics : PROCEEDINGS},
	doi = {10.1109/SACI46893.2019.9111596},
	unique-id = {31605588},
	year = {2019},
	pages = {143-148},
	orcid-numbers = {Dénes-Fazakas, Lehel/0009-0003-9879-5664}
}

@article{MTMT:30644610,
	title = {Intrusion detection using mouse dynamics},
	url = {https://m2.mtmt.hu/api/publication/30644610},
	author = {Antal, Margit and Egyed-Zsigmond, Elöd},
	doi = {10.1049/iet-bmt.2018.5126},
	journal-iso = {IET BIOMETRICS},
	journal = {IET BIOMETRICS},
	volume = {8},
	unique-id = {30644610},
	issn = {2047-4938},
	year = {2019},
	eissn = {2047-4946},
	pages = {285-294}
}

@inproceedings{MTMT:30644661,
	title = {Járásfelismerés gyorsulásérzékelő segítségével},
	url = {https://m2.mtmt.hu/api/publication/30644661},
	author = {Antal, Margit and Fülöp, Timea and Mille, János and Németh, Krisztián},
	booktitle = {ENELKO 2018 XIX. Nemzetközi Energetika-Elektrotechnika Konferencia SzámOkt 2018 XXVIII. Nemzetközi Számítástechnika és Oktatás Konferencia},
	unique-id = {30644661},
	year = {2018},
	pages = {118-123}
}

@inproceedings{MTMT:30644645,
	title = {Performance Evaluation of Gaussian Mixture Models for Inertial Sensor-based Gait Biometrics},
	url = {https://m2.mtmt.hu/api/publication/30644645},
	author = {Antal, Margit},
	booktitle = {IEEE 18th International Symposium on Computational Intelligence and Informatics (CINTI 2018)},
	unique-id = {30644645},
	year = {2018},
	pages = {143-147}
}

@article{MTMT:3336556,
	title = {Online Signature Verification on MOBISIG Finger-Drawn Signature Corpus},
	url = {https://m2.mtmt.hu/api/publication/3336556},
	author = {Antal, Margit and Szabó, László Zsolt and Tordai, Tünde},
	doi = {10.1155/2018/3127042},
	journal-iso = {MOB INF SYST},
	journal = {MOBILE INFORMATION SYSTEMS},
	volume = {2018},
	unique-id = {3336556},
	issn = {1574-017X},
	year = {2018},
	eissn = {1875-905X}
}