Avaliando Ataques Adversariais em Aplicações de Interfaces Cérebro-Computador
Resumo
Interfaces cérebro-computador (BCIdo inglês brain computer interface) são sistemas que captam sinais cerebrais através de técnicas como eletroencefalografia (EEG), processando esses sinais para diversas aplicações, especialmente no controle de dispositivos para pessoas com limitações motoras. Apesar dos benefícios, há preocupações com segurança, incluindo ataques adversarias e de segurança cibernética. A proteção dos dados pessoais dos usuários é crucial, exigindo pesquisas contínuas em cibersegurança para mitigar riscos e garantir a privacidade nas aplicações BCI. Durante a avaliação, podemos identificar efeitos negativos na classificação de dados produzidos por ataques adversariais. Em virtude do surgimento de dispositivos de interação cérebro computador no mercado, que acessam ondas cerebrais dos usuários e os analisam para diversos propósitos, se faz necessário a análise do quesito segurança desses dispositivos. Diante disto, este trabalho tem como objetivo emular e analisar ataques adversariais em classificadores de dispositivos interface cérebro computador.
Palavras-chave:
Segurança, interface cérebro computador, ataques adversariais
Referências
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Jung, J. et al. Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces. IEEE Journal of Biomedical and Health Informatics, v. 27, n. 11, p. 5622–5633, 2023. DOI: 10.1109/JBHI.2023.3303494.
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Martínez Beltrán, E. T. et al. Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces. Cluster Computing, Springer, p. 1–16, 2021.
Amorim, M. et al. Systematic Review of Aggregation Functions Applied to Image Edge Detection. Axioms, v. 12, n. 4, 2023. ISSN 2075-1680. DOI: 10.3390/axioms12.040330. Disponível em: [link].
Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering, IOP Publishing, v. 15, n. 5, p. 056013, jul. 2018. DOI: 10.1088/1741-2552/aace8c.
Dalmazo, B. L.; Vilela, J. P.; Curado, M. Performance analysis of network traffic predictors in the cloud. Journal of Network and Systems Management, Springer, v. 25, p. 290–320, 2017.
Dalmazo, B. L.; Vilela, J. P.; Curado, M. Triple-Similarity Mechanism for alarm management in the cloud. Computers & Security, v. 78, p. 33–42, 2018. ISSN 0167-4048. DOI: 10.1016/j.cose.2018.05.016. Disponível em: [link].
Neurable. Neurable. 2024. Disponível em: [link].
Neuralink. Neuralink. 2024. Disponível em: [link].
Neurosity. Neurosity. 2024. Disponível em: [link].
Jiang, X.; Zhang, X.; Wu, D. Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019. P. 361–368. DOI: 10.1109/SSCI44817.2019.9002719.
Jung, J. et al. Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces. IEEE Journal of Biomedical and Health Informatics, v. 27, n. 11, p. 5622–5633, 2023. DOI: 10.1109/JBHI.2023.3303494.
Upadhayay, B.; Behzadan, V. Adversarial Stimuli: Attacking Brain-Computer Interfaces via Perturbed Sensory Events. In: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2023. P. 3061–3066. DOI: 10.1109/SMC53992.2023.10394505.
Feng, B.; Wang, Y.; Ding, Y. Saga: Sparse Adversarial Attack on EEG-Based Brain Computer Interface. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2021. P. 975–979. DOI: 10.1109/ICASSP39728.2021.9413507.
Aissa, N. E. H. S. B. et al. Enhancing EEG Signal Classifier Robustness Against Adversarial Attacks Using a Generative Adversarial Network Approach. IEEE Internet of Things Magazine, v. 7, n. 3, p. 44–49, 2024. DOI: 10.1109/IOTM.001.2300262.
Li, Y. et al. Adversarial Training for the Adversarial Robustness of EEG-Based Brain-Computer Interfaces. In: 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP). 2022. P. 1–6. DOI: 10.1109/MLSP55214.2022.9943479.
Aissa, N. E. H. S. B. et al. Robust Detection of Adversarial Attacks for EEG-based Motor Imagery Classification using Hierarchical Deep Learning. In: 2023 15th International Conference on Innovations in Information Technology (IIT). 2023. P. 156–161. DOI: 10.1109/IIT59782.2023.10366492.
Debie, E.; Moustafa, N.; Whitty, M. T. A Privacy-Preserving Generative Adversarial Network Method for Securing EEG Brain Signals. In: 2020 International Joint Conference on Neural Networks (IJCNN). 2020. P. 1–8. DOI: 10.1109/IJCNN48605.2020.9206683.
Leite, L. et al. Federated Learning under Attack: Improving Gradient Inversion for Batch of Images. In: ANAIS do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. São José dos Campos/SP: SBC, 2024. P. 794–800. DOI: 10.5753/sbseg.2024.241680. Disponível em: [link].
Kaya, M. et al. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces. 2018. Wiki do abnTeX2. Disponível em: [link].
Beltrán, E. T. M. et al. SecBrain: A Framework to Detect Cyberattacks Revealing Sensitive Data in Brain-Computer Interfaces. In: ADVANCES in Malware and Data-Driven Network Security. IGI Global, 2022. P. 176–198.
Martínez Beltrán, E. T. et al. Noise-based cyberattacks generating fake P300 waves in brain–computer interfaces. Cluster Computing, Springer, p. 1–16, 2021.
Amorim, M. et al. Systematic Review of Aggregation Functions Applied to Image Edge Detection. Axioms, v. 12, n. 4, 2023. ISSN 2075-1680. DOI: 10.3390/axioms12.040330. Disponível em: [link].
Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of Neural Engineering, IOP Publishing, v. 15, n. 5, p. 056013, jul. 2018. DOI: 10.1088/1741-2552/aace8c.
Publicado
27/11/2024
Como Citar
COSTA, Beatriz; RIKER, André; DALMAZO, Bruno L..
Avaliando Ataques Adversariais em Aplicações de Interfaces Cérebro-Computador. In: ESCOLA REGIONAL DE REDES DE COMPUTADORES (ERRC), 21. , 2024, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
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p. 123-128.
DOI: https://doi.org/10.5753/errc.2024.4583.
