{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:49:45Z","timestamp":1747457385891,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616373"},{"type":"electronic","value":"9783030616380"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61638-0_13","type":"book-chapter","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T23:08:28Z","timestamp":1602630508000},"page":"217-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Forgotten Hyperparameter:"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-7457","authenticated-orcid":false,"given":"Servio","family":"Paguada","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-455X","authenticated-orcid":false,"given":"Igor","family":"Armendariz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"13_CR1","unstructured":"Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems, software available from tensorflow.org (2015). https:\/\/www.tensorflow.org\/"},{"key":"13_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-319-08302-5_8","volume-title":"Smart Card Research and Advanced Applications","author":"P Belgarric","year":"2014","unstructured":"Belgarric, P., et al.: Time-frequency analysis for second-order attacks. In: Francillon, A., Rohatgi, P. (eds.) CARDIS 2013. LNCS, vol. 8419, pp. 108\u2013122. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-08302-5_8"},{"key":"13_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-540-30564-4_5","volume-title":"Selected Areas in Cryptography","author":"J Bl\u00f6mer","year":"2004","unstructured":"Bl\u00f6mer, J., Guajardo, J., Krummel, V.: Provably secure masking of AES. In: Handschuh, H., Hasan, M.A. (eds.) SAC 2004. LNCS, vol. 3357, pp. 69\u201383. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-30564-4_5"},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-66787-4_3","volume-title":"Cryptographic Hardware and Embedded Systems \u2013 CHES 2017","author":"E Cagli","year":"2017","unstructured":"Cagli, E., Dumas, C., Prouff, E.: Convolutional neural networks with data augmentation against jitter-based countermeasures. In: Fischer, W., Homma, N. (eds.) CHES 2017. LNCS, vol. 10529, pp. 45\u201368. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66787-4_3"},{"key":"13_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-54669-8_1","volume-title":"Smart Card Research and Advanced Applications","author":"E Cagli","year":"2017","unstructured":"Cagli, E., Dumas, C., Prouff, E.: Kernel discriminant analysis for information extraction in the presence of masking. In: Lemke-Rust, K., Tunstall, M. (eds.) CARDIS 2016. LNCS, vol. 10146, pp. 1\u201322. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54669-8_1"},{"key":"13_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/3-540-36400-5_3","volume-title":"Cryptographic Hardware and Embedded Systems - CHES 2002","author":"S Chari","year":"2003","unstructured":"Chari, S., Rao, J.R., Rohatgi, P.: Template attacks. In: Kaliski, B.S., Ko\u00e7, K., Paar, C. (eds.) CHES 2002. LNCS, vol. 2523, pp. 13\u201328. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-36400-5_3"},{"issue":"4","key":"13_CR7","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Choi, K., Fazekas, G., Sandler, M., Cho, K.: Convolutional recurrent neural networks for music classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2392\u20132396. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952585"},{"key":"13_CR9","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"issue":"2","key":"13_CR10","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/TIFS.2017.2757440","volume":"13","author":"MO Choudary","year":"2018","unstructured":"Choudary, M.O., Kuhn, M.G.: Efficient, portable template attacks. IEEE Trans. Inf. Forensics Secur. 13(2), 490\u2013501 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-662-43933-3_21","volume-title":"Fast Software Encryption","author":"JS Coron","year":"2014","unstructured":"Coron, J.S., Prouff, E., Rivain, M., Roche, T.: Higher-order side channel security and mask refreshing. In: Moriai, S. (ed.) Fast Software Encryption, pp. 410\u2013424. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-43933-3_21"},{"key":"13_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-04722-4","volume-title":"The Design of Rijndael","author":"J Daemen","year":"2002","unstructured":"Daemen, J., Rijmen, V.: The Design of Rijndael. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/978-3-662-04722-4"},{"key":"13_CR13","unstructured":"Dumoulin, V., Visin, F.: A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016)"},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/978-3-319-27998-5_11","volume-title":"Trusted Systems","author":"G Fan","year":"2015","unstructured":"Fan, G., Zhou, Y., Zhang, H., Feng, D.: How to choose interesting points for template attacks more effectively? In: Yung, M., Zhu, L., Yang, Y. (eds.) INTRUST 2014. LNCS, vol. 9473, pp. 168\u2013183. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-27998-5_11"},{"key":"13_CR15","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press (2016)"},{"key":"13_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-12087-4_21","volume-title":"Information Security and Cryptology","author":"S Hajra","year":"2014","unstructured":"Hajra, S., Mukhopadhyay, D.: Multivariate leakage model for improving non-profiling DPA on noisy power traces. In: Lin, D., Xu, S., Yung, M. (eds.) Inscrypt 2013. LNCS, vol. 8567, pp. 325\u2013342. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-12087-4_21"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Hamaguchi, R., Fujita, A., Nemoto, K., Imaizumi, T., Hikosaka, S.: Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1442\u20131450 (2018)","DOI":"10.1109\/WACV.2018.00162"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Hettwer, B., Gehrer, S., G\u00fcneysu, T.: Profiled power analysis attacks using convolutional neural networks with domain knowledge. In: Selected Areas in Cryptography - SAC 2018\u201325th International Conference, Calgary, AB, Canada, 15\u201317 August 2018, Revised Selected Papers, pp. 479\u2013498 (2018)","DOI":"10.1007\/978-3-030-10970-7_22"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1007\/978-3-030-38471-5_26","volume-title":"Selected Areas in Cryptography \u2013 SAC 2019","author":"B Hettwer","year":"2020","unstructured":"Hettwer, B., Gehrer, S., G\u00fcneysu, T.: Deep neural network attribution methods for leakage analysis and symmetric key recovery. In: Paterson, K.G., Stebila, D. (eds.) SAC 2019. LNCS, vol. 11959, pp. 645\u2013666. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-38471-5_26"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Kim, J., Picek, S., Heuser, A., Bhasin, S., Hanjalic, A.: Make some noise: unleashing the power of convolutional neural networks for profiled side-channel analysis. IACR Cryptology ePrint Archive 2018, 1023 (2018)","DOI":"10.1007\/978-3-030-05072-6_10"},{"key":"13_CR21","unstructured":"Maghrebi, H.: Deep learning based side channel attacks in practice. IACR Cryptology ePrint Archive 2019, 578 (2019)"},{"key":"13_CR22","doi-asserted-by":"publisher","unstructured":"Mangard, S., Oswald, E., Popp, T.: Power Analysis Attacks: Revealing the Secrets of Smart Cards, vol. 31. Springer, Boston (2008). https:\/\/doi.org\/10.1007\/978-0-387-38162-6","DOI":"10.1007\/978-0-387-38162-6"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Martinasek, Z., Dzurenda, P., Malina, L.: Profiling power analysis attack based on MLP in DPA contest V4.2. In: 2016 39th International Conference on Telecommunications and Signal Processing (TSP), pp. 223\u2013226 (2016)","DOI":"10.1109\/TSP.2016.7760865"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Martinasek, Z., Zapletal, O., Vrba, K., Trasy, K.: Power analysis attack based on the MLP in DPA contest v4 (07 2015)","DOI":"10.1109\/TSP.2015.7296242"},{"key":"13_CR25","doi-asserted-by":"publisher","unstructured":"Masure, L., Dumas, C., Prouff, E.: Gradient visualization for general characterization in profiling attacks. In: Polian, I., St\u00f6ttinger, M. (eds.) International Workshop on Constructive Side-Channel Analysis and Secure Design, pp. 145\u2013167. Springer (2019). https:\/\/doi.org\/10.1007\/978-3-030-16350-1_9","DOI":"10.1007\/978-3-030-16350-1_9"},{"key":"13_CR26","unstructured":"Masure, L., Dumas, C., Prouff, E.: A comprehensive study of deep learning for side-channel analysis. IACR Trans. Cryptographic Hardware Embed. Syst. 2020, 348\u2013375 (2020)"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Ng, A.Y.: Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 78. Association for Computing Machinery, New York (2004)","DOI":"10.1145\/1015330.1015435"},{"key":"13_CR28","unstructured":"van den Oord, A., et al.: WaveNet: a generative model for raw audio. In: SSW (2016)"},{"key":"13_CR29","unstructured":"Perin, G., Ege, B., Chmielewski, L.: Neural Network Model Assessment for Side-Channel Analysis. IACR Cryptology ePrint Archive 2019, 722 (2019)"},{"key":"13_CR30","unstructured":"Picek, S., Heuser, A., Jovic, A., Batina, L., Legay, A.: The secrets of profiling for side-channel analysis: feature selection matters. IACR Cryptology ePrint Archive 2017, 1110 (2017)"},{"key":"13_CR31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.46586\/tches.v2019.i1.209-237","volume":"2019","author":"S Picek","year":"2018","unstructured":"Picek, S., Heuser, A., Jovic, A., Bhasin, S., Regazzoni, F.: The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019, 209\u2013237 (2018)","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"13_CR32","unstructured":"Prouff, E., Strullu, R., Benadjila, R., Cagli, E., Canovas, C.: Study of deep learning techniques for side-channel analysis and introduction to ASCAD database. IACR Cryptology ePrint Archive 2018, 53 (2018)"},{"key":"13_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/978-3-540-31815-6_35","volume-title":"Information Security Applications","author":"C Rechberger","year":"2005","unstructured":"Rechberger, C., Oswald, E.: Practical template attacks. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 440\u2013456. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31815-6_35"},{"key":"13_CR34","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"key":"13_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/978-3-642-01001-9_26","volume-title":"Advances in Cryptology - EUROCRYPT 2009","author":"F-X Standaert","year":"2009","unstructured":"Standaert, F.-X., Malkin, T.G., Yung, M.: A unified framework for the analysis of side-channel key recovery attacks. In: Joux, A. (ed.) EUROCRYPT 2009. LNCS, vol. 5479, pp. 443\u2013461. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-01001-9_26"},{"key":"13_CR36","unstructured":"Thiebeauld, H., Vasselle, A., Wurcker, A.: Second-order scatter attack. IACR Cryptology ePrint Archive 2019, 345 (2019)"},{"issue":"2","key":"13_CR37","doi-asserted-by":"crossref","first-page":"107","DOI":"10.46586\/tches.v2019.i2.107-131","volume":"2019","author":"B Timon","year":"2019","unstructured":"Timon, B.: Non-profiled deep learning-based side-channel attacks with sensitivity analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2019(2), 107\u2013131 (2019)","journal-title":"IACR Trans. Cryptogr. Hardw. Embed. Syst."},{"key":"13_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-540-28632-5_1","volume-title":"Cryptographic Hardware and Embedded Systems - CHES 2004","author":"J Waddle","year":"2004","unstructured":"Waddle, J., Wagner, D.: Towards efficient second-order power analysis. In: Joye, M., Quisquater, J.-J. (eds.) CHES 2004. LNCS, vol. 3156, pp. 1\u201315. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-28632-5_1"},{"key":"13_CR39","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. CoRR abs\/1511.07122 (2016)"},{"issue":"1","key":"13_CR40","first-page":"1","volume":"2020","author":"G Zaid","year":"2019","unstructured":"Zaid, G., Bossuet, L., Habrard, A., Venelli, A.: Methodology for efficient CNN architectures in profiling attacks. IACR Trans. Cryptographic Hardware Embed. Syst. 2020(1), 1\u201336 (2019)","journal-title":"IACR Trans. Cryptographic Hardware Embed. Syst."}],"container-title":["Lecture Notes in Computer Science","Applied Cryptography and Network Security Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61638-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T16:46:43Z","timestamp":1617900403000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61638-0_13"}},"subtitle":["Introducing Dilated Convolution for Boosting CNN-Based Side-Channel Attacks"],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616373","9783030616380"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61638-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACNS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Cryptography and Network Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acns2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/di.uniroma1.it\/ACNS2020","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"214","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.7","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}