{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:03:22Z","timestamp":1789531402992,"version":"build-2803163510"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2022YFB4400700"],"award-info":[{"award-number":["2022YFB4400700"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["NO.tsqn202408026"],"award-info":[{"award-number":["NO.tsqn202408026"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.62472259"],"award-info":[{"award-number":["NO.62472259"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.62572285"],"award-info":[{"award-number":["NO.62572285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NO.62172258"],"award-info":[{"award-number":["NO.62172258"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024QF098"],"award-info":[{"award-number":["ZR2024QF098"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Post-quantum cryptographic implementations are increasingly exposed to side-channel threats, making implementation-level security assessment an essential complement to algorithmic analysis. BIKE is a representative code-based key encapsulation mechanism, and its latest specification adopts the Black-Gray-Flip (BGF) decoder, which offers practical advantages in decoding performance and implementation regularity. However, the side-channel security of this decoding scheme has not been adequately studied in the latest BIKE reference implementation. In this work, we investigate this problem and develop a deep-learning-based side-channel attack for recovering a private-key-dependent sequence from long BIKE decoding traces. Firstly, we examine the BGF decoding procedure in the latest BIKE reference implementation and show that its repeated counter computations and threshold-based bit-flipping decisions give rise to exploitable side-channel leakage. Secondly, we transform BIKE BGF decoding leakage recovery into an alignment-free sequence-to-sequence task and instantiate it with a convolutional recurrent neural network (CRNN) trained using Connectionist Temporal Classification (CTC). This formulation enables direct recovery of the target sequence from complete traces without relying on manually defined temporal boundaries. Finally, we evaluate the proposed formulation through end-to-end recovery experiments, ablation analysis, and comparison with representative baselines. In the evaluated profiled, unprotected, same-platform ARM Cortex-M4 setting, experimental results show that the proposed method achieves 99.69% bit accuracy and 95.68% perfect sequence recovery on the independent test set. Our target is a 16-bit sequence of counter-comparison outcomes generated during BIKE decoding; this sequence is a private-key-dependent intermediate rather than the complete private key. Complete private-key recovery would require accumulating such observations over multiple executions and applying an additional post-processing stage. Overall, these results show that alignment-free sequence recovery is effective for BIKE side-channel analysis on long, temporally unstable traces in this evaluated setting.<\/jats:p>","DOI":"10.1186\/s42400-026-00653-9","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T03:08:56Z","timestamp":1789528136000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning based side-channel attack on BIKE"],"prefix":"10.1186","volume":"9","author":[{"given":"Geng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7151-9270","authenticated-orcid":false,"given":"Yanbin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongyue","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunpeng","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fusheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiuliang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"653_CR1","unstructured":"Aragon N, Barreto PSLM, Bettaieb S, Bidoux L, Blazy O, Deneuville J-C, Gaborit P, Ghosh S, Gueron S, G\u00fcneysu T, Aguilar Melchor C, Misoczki R, Persichetti E, Richter-Brockmann J, Sendrier N, Tillich J-P, Vasseur V, Z\u00e9mor G (2024) BIKE: bit flipping key encapsulation, Round 4 Submission. NIST Post-Quantum Cryptography Standardization Project submission (version 5.2)"},{"key":"653_CR2","doi-asserted-by":"publisher","unstructured":"Beckwith L, Hettwer B, G\u00fcneysu T (2024) Power side-channel key recovery attack on a hardware implementation of bike. In: IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp 267\u2013277. IEEE, Washington, DC, USA.https:\/\/doi.org\/10.1109\/HOST55342.2024.10545264","DOI":"10.1109\/HOST55342.2024.10545264"},{"issue":"2","key":"653_CR3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s13389-019-00220-8","volume":"10","author":"R Benadjila","year":"2020","unstructured":"Benadjila R, Prouff E, Strullu R, Cagli E, Dumas C (2020) Deep learning for side-channel analysis and introduction to the ASCAD database. J Cryptogr Eng 10(2):163\u2013188. https:\/\/doi.org\/10.1007\/s13389-019-00220-8","journal-title":"J Cryptogr Eng"},{"key":"653_CR4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-66787-4_3","volume-title":"Cryptographic Hardware and Embedded Systems\u2014CHES 2017","author":"C Cagli","year":"2017","unstructured":"Cagli C, Eleonora I, Prouff E (2017) Convolutional neural networks with data augmentation against jitter-based countermeasures. Cryptographic Hardware and Embedded Systems\u2014CHES 2017, vol vol 10529. Springer, Cham, pp 45\u201368. https:\/\/doi.org\/10.1007\/978-3-319-66787-4_3"},{"key":"653_CR5","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-031-33491-7_4","volume-title":"Applied cryptography and network security","author":"C Cheriere","year":"2023","unstructured":"Cheriere C, Rivain M, Rossi M, Vaslin R (2023) Bike key-recovery: combining power consumption analysis and information-set decoding. In: Tibouchi M, Wang X (eds) Applied cryptography and network security. Springer, Cham, pp 64\u201384. https:\/\/doi.org\/10.1007\/978-3-031-33491-7_4"},{"issue":"4","key":"653_CR6","first-page":"1879","volume":"28","author":"KA Delgado-Vargas","year":"2024","unstructured":"Delgado-Vargas KA, Mancillas-L\u00f3pez C, Gallegos-Garc\u00eda G (2024) A look at side channel attacks on post-quantum cryptography. Comput Sist 28(4):1879\u20131896","journal-title":"Comput Sist"},{"issue":"5","key":"653_CR7","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1109\/TC.2021.3075217","volume":"71","author":"Q Guo","year":"2022","unstructured":"Guo Q, Johansson T, Nilsson A (2022) A key-recovery timing attack on post-quantum primitive BIKE. 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Springer, Berlin, Heidelberg, pp 388\u2013397. https:\/\/doi.org\/10.1007\/3-540-48405-1_25"},{"key":"653_CR10","doi-asserted-by":"publisher","first-page":"235","DOI":"10.13154\/tches.v2021.i3.235-274","volume":"3","author":"X Lu","year":"2021","unstructured":"Lu X, Zhang C, Cao P, Gu D, Lu H (2021) Pay attention to raw traces: a deep learning architecture for end-to-end profiling attacks. IACR Trans Cryptogr Hardw Embed Syst 3:235\u2013274. https:\/\/doi.org\/10.13154\/tches.v2021.i3.235-274","journal-title":"IACR Trans Cryptogr Hardw Embed Syst"},{"key":"653_CR11","unstructured":"Maghrebi H (2019) Deep learning based side channel attacks in practice. Cryptology ePrint Archive, Report 2019\/578. https:\/\/eprint.iacr.org\/2019\/578"},{"key":"653_CR12","doi-asserted-by":"publisher","unstructured":"Maghrebi H, Portigliatti T, Prouff E (2016) Breaking cryptographic implementations using deep learning techniques. In: Security, Privacy, and Applied Cryptography Engineering (SPACE 2016). 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