{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T10:14:00Z","timestamp":1766139240834,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031995644"},{"type":"electronic","value":"9783031995651"}],"license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-99565-1_31","type":"book-chapter","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T06:42:03Z","timestamp":1753771323000},"page":"403-414","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spiking Alternatives for\u00a0the\u00a0Leaky Integrate-and-Fire Neuron: Applications in\u00a0Cybersecurity and\u00a0Financial Threats"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6923-900X","authenticated-orcid":false,"given":"Dylan","family":"Perdig\u00e3o","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5286-866X","authenticated-orcid":false,"given":"Francisco","family":"Antunes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5656-0061","authenticated-orcid":false,"given":"Catarina","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9770-7672","authenticated-orcid":false,"given":"Bernardete","family":"Ribeiro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a Next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2623\u20132631. KDD \u201919, Association for Computing Machinery, New York, USA (2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"issue":"1","key":"31_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00422-006-0068-6","volume":"95","author":"AN Burkitt","year":"2006","unstructured":"Burkitt, A.N.: A review of the integrate-and-fire neuron model: I. Homogeneous Synaptic Input. Biol. Cybern. 95(1), 1\u201319 (2006). https:\/\/doi.org\/10.1007\/s00422-006-0068-6","journal-title":"Homogeneous Synaptic Input. Biol. Cybern."},{"key":"31_CR3","doi-asserted-by":"publisher","unstructured":"Cort\u00eas, G., Louren\u00e7o, N., Machado, P.: Towards physical plausibility in\u00a0neuroevolution systems. In: Smith, S., Correia, J., Cintrano, C. (eds.) Applications of Evolutionary Computation, pp. 76\u201390. Springer Nature Switzerland, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-56855-8_5","DOI":"10.1007\/978-3-031-56855-8_5"},{"issue":"10","key":"31_CR4","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1016\/j.joule.2023.09.004","volume":"7","author":"A de Vries","year":"2023","unstructured":"de Vries, A.: The growing energy footprint of artificial intelligence. Joule 7(10), 2191\u20132194 (2023). https:\/\/doi.org\/10.1016\/j.joule.2023.09.004","journal-title":"Joule"},{"issue":"9","key":"31_CR5","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1109\/JPROC.2023.3308088","volume":"111","author":"JK Eshraghian","year":"2023","unstructured":"Eshraghian, J.K., et al.: Training spiking neural networks using lessons from deep learning. Proc. IEEE 111(9), 1016\u20131054 (2023). https:\/\/doi.org\/10.1109\/JPROC.2023.3308088","journal-title":"Proc. IEEE"},{"issue":"4","key":"31_CR6","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/BF02477753","volume":"17","author":"R FitzHugh","year":"1955","unstructured":"FitzHugh, R.: Mathematical models of threshold phenomena in the nerve membrane. Bull. Math. Biophys. 17(4), 257\u2013278 (1955). https:\/\/doi.org\/10.1007\/BF02477753","journal-title":"Bull. Math. Biophys."},{"key":"31_CR7","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815706","volume-title":"Spiking Neuron Models: Single Neurons, Populations, Plasticity","author":"W Gerstner","year":"2002","unstructured":"Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)"},{"key":"31_CR8","doi-asserted-by":"publisher","unstructured":"Gerstner, W., Kistler, W.M., Naud, R., Paninski, L.: Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press, Cambridge (2014). https:\/\/doi.org\/10.1017\/CBO9781107447615","DOI":"10.1017\/CBO9781107447615"},{"issue":"4","key":"31_CR9","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1113\/jphysiol.1952.sp004764","volume":"117","author":"AL Hodgkin","year":"1952","unstructured":"Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500\u2013544 (1952). https:\/\/doi.org\/10.1113\/jphysiol.1952.sp004764","journal-title":"J. Physiol."},{"issue":"6","key":"31_CR10","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"E Izhikevich","year":"2003","unstructured":"Izhikevich, E.: Simple model of spiking neurons. IEEE Trans. Neural Networks 14(6), 1569\u20131572 (2003). https:\/\/doi.org\/10.1109\/TNN.2003.820440","journal-title":"IEEE Trans. Neural Networks"},{"issue":"5","key":"31_CR11","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1109\/TNN.2004.832719","volume":"15","author":"E Izhikevich","year":"2004","unstructured":"Izhikevich, E.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks 15(5), 1063\u20131070 (2004). https:\/\/doi.org\/10.1109\/TNN.2004.832719","journal-title":"IEEE Trans. Neural Networks"},{"key":"31_CR12","unstructured":"Jesus, S., et al.: Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. In: Neural Information Processing Systems (NeurIPS). vol.\u00a035, pp. 33563\u201333575. Curran Associates, Inc. (2022)"},{"key":"31_CR13","unstructured":"Lapicque, L.: Recherches quantitatives sur l\u2019excitation \u00e9lectrique des nerfs trait\u00e9e comme une polarisation. J. de Physiol. et de Pathol. g\u00e9n\u00e9rale 1\u201316 (1907)"},{"key":"31_CR14","doi-asserted-by":"publisher","unstructured":"Lim, Y.s.: Empirical evaluation of SNN for IoT network anomaly detection. 49(11), 1497\u20131509 (2024). https:\/\/doi.org\/10.7840\/kics.2024.49.11.1497","DOI":"10.7840\/kics.2024.49.11.1497"},{"issue":"9","key":"31_CR15","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659\u20131671 (1997). https:\/\/doi.org\/10.1016\/S0893-6080(97)00011-7","journal-title":"Neural Netw."},{"issue":"4","key":"31_CR16","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115\u2013133 (1943). https:\/\/doi.org\/10.1007\/BF02478259","journal-title":"Bull. Math. Biophys."},{"key":"31_CR17","doi-asserted-by":"publisher","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp.\u00a01\u20136 (2015). https:\/\/doi.org\/10.1109\/MilCIS.2015.7348942","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"6","key":"31_CR18","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6), 51\u201363 (2019). https:\/\/doi.org\/10.1109\/MSP.2019.2931595","journal-title":"IEEE Signal Process. Mag."},{"key":"31_CR19","doi-asserted-by":"publisher","unstructured":"Perdig\u00e3o, D., Antunes, F., Silva, C., Ribeiro, B.: Exploring neural joint activity in\u00a0spiking neural networks for\u00a0fraud detection. In: Hern\u00e1ndez-Garc\u00eda, R., Barrientos, R.J., Velastin, S.A. (eds.) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 45\u201359. Springer Nature Switzerland, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-76604-6_4","DOI":"10.1007\/978-3-031-76604-6_4"},{"key":"31_CR20","doi-asserted-by":"publisher","unstructured":"Perdig\u00e3o, D., Antunes, F., Silva, C., Ribeiro, B.: Improving fraud detection with\u00a01D-convolutional spiking neural networks through bayesian optimization. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds.) Progress in Artificial Intelligence, pp. 127\u2013138. Springer Nature Switzerland, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-73503-5_11","DOI":"10.1007\/978-3-031-73503-5_11"},{"key":"31_CR21","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.patrec.2024.08.002","volume":"189","author":"B Ribeiro","year":"2025","unstructured":"Ribeiro, B., Antunes, F., Perdig\u00e3o, D., Silva, C.: Convolutional spiking neural networks targeting learning and inference in highly imbalanced datasets. Pattern Recogn. Lett. 189, 241\u2013247 (2025). https:\/\/doi.org\/10.1016\/j.patrec.2024.08.002","journal-title":"Pattern Recogn. Lett."},{"key":"31_CR22","doi-asserted-by":"publisher","unstructured":"Sun, P.S.V., et al.: Intelligence Processing Units Accelerate Neuromorphic Learning (2022). https:\/\/doi.org\/10.48550\/arXiv.2211.10725","DOI":"10.48550\/arXiv.2211.10725"},{"key":"31_CR23","doi-asserted-by":"publisher","unstructured":"Watanabe, S.: Tree-Structured Parzen Estimator: understanding its algorithm components and their roles for better empirical performance (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.11127","DOI":"10.48550\/arXiv.2304.11127"},{"issue":"4","key":"31_CR24","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1006\/jtbi.1999.1002","volume":"200","author":"HR Wilson","year":"1999","unstructured":"Wilson, H.R.: Simplified dynamics of human and mammalian neocortical neurons. J. Theor. Biol. 200(4), 375\u2013388 (1999). https:\/\/doi.org\/10.1006\/jtbi.1999.1002","journal-title":"J. Theor. Biol."},{"key":"31_CR25","unstructured":"Xu, M., et al.: A survey of resource-efficient llm and multimodal foundation models. CoRR (2024)"},{"key":"31_CR26","doi-asserted-by":"publisher","unstructured":"Zarzoor, A.R., Al-Jamali, N.A.S., Qader, D.A.A.: Intrusion detection method for internet of things based on the spiking neural network and decision tree method. Int. J. Electr. Comput. Eng. (IJECE) 13(2), 2278\u20132288 (2023). https:\/\/doi.org\/10.11591\/ijece.v13i2.pp2278-2288","DOI":"10.11591\/ijece.v13i2.pp2278-2288"},{"key":"31_CR27","doi-asserted-by":"publisher","unstructured":"Zhou, S., Li, X.: Spiking neural networks with single-spike temporal-coded neurons for network intrusion detection. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8148\u20138155 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412580","DOI":"10.1109\/ICPR48806.2021.9412580"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-99565-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:21:24Z","timestamp":1757312484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-99565-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"ISBN":["9783031995644","9783031995651"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-99565-1_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,7,30]]},"assertion":[{"value":"30 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ibpria.org\/2025\/?page=home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}