{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T17:15:03Z","timestamp":1771866903083,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Guangxi Science and Technology Plan","award":["2023AB01361"],"award-info":[{"award-number":["2023AB01361"]}]},{"name":"Wuzhou University Research Project","award":["2025QN028"],"award-info":[{"award-number":["2025QN028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear O(N) complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution.<\/jats:p>","DOI":"10.3390\/s26041399","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T16:22:55Z","timestamp":1771863775000},"page":"1399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LCSMC-Net: Lightweight CAN Intrusion Detection via Separable Multiscale Convolution and Attention"],"prefix":"10.3390","volume":"26","author":[{"given":"Mengdi","family":"Hou","sequence":"first","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, Wuzhou University, Wuzhou 543000, China"},{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bitie","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Artificial Intelligence, Wuzhou University, Wuzhou 543000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghua","family":"Tang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbo","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Application, Guilin University of Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"ref_1","first-page":"3","article-title":"This car runs on code","volume":"46","author":"Charette","year":"2009","journal-title":"IEEE Spectr."},{"key":"ref_2","unstructured":"Robert Bosch GmbH (1991). 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