{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:25:53Z","timestamp":1773062753040,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,6]]},"DOI":"10.1145\/3526241.3530326","type":"proceedings-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T14:37:09Z","timestamp":1654180629000},"page":"27-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Neural Network and Transfer Learning for Accurate Hardware-Based Zero-Day Malware Detection"],"prefix":"10.1145","author":[{"given":"Zhangying","family":"He","sequence":"first","affiliation":[{"name":"California State University, Long Beach, Long Beach, CA, USA"}]},{"given":"Amin","family":"Rezaei","sequence":"additional","affiliation":[{"name":"California State University, Long Beach, Long Beach, CA, USA"}]},{"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"University of California, Davis, Davis, CA, USA"}]},{"given":"Hossein","family":"Sayadi","sequence":"additional","affiliation":[{"name":"California State University, Long Beach, Long Beach, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2382196.2382284"},{"key":"e_1_3_2_1_2_1","volume-title":"The OpenCV Library. Dr. Dobb's Journal of Software Tools","author":"Bradski G.","year":"2000","unstructured":"G. Bradski . 2000. The OpenCV Library. Dr. Dobb's Journal of Software Tools ( 2000 ). G. Bradski. 2000. The OpenCV Library. Dr. Dobb's Journal of Software Tools (2000)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"S. Das etal 2019. SoK: The Challenges Pitfalls and Perils of Using Hardware Performance Counters for Security. In IEEE SP. 20--38.  S. Das et al. 2019. SoK: The Challenges Pitfalls and Perils of Using Hardware Performance Counters for Security. In IEEE SP. 20--38.","DOI":"10.1109\/SP.2019.00021"},{"key":"e_1_3_2_1_4_1","volume-title":"On the Feasibility of Online Malware Detection with Performance Counters. In ISCA'13","author":"Demme J.","year":"2013","unstructured":"J. Demme 2013 . On the Feasibility of Online Malware Detection with Performance Counters. In ISCA'13 . ACM, 559--570. J. Demme et al. 2013. On the Feasibility of Online Malware Detection with Performance Counters. In ISCA'13. ACM, 559--570."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3844\/ajassp.2012.283.288"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/IOLTS52814.2021.9486701"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/WWC.2001.990739"},{"key":"e_1_3_2_1_8_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778","author":"He K.","year":"2016","unstructured":"K. He 2016 . Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778 . K. He et al. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISQED51717.2021.9424330"},{"key":"e_1_3_2_1_10_1","volume-title":"SPEC CPU2006 Benchmark Descriptions. SIGARCH Comput. Archit. News 34","author":"Henning J. L.","year":"2006","unstructured":"J. L. Henning . 2006 . SPEC CPU2006 Benchmark Descriptions. SIGARCH Comput. Archit. News 34 , 4 (Sept. 2006), 1--17. J. L. Henning. 2006. SPEC CPU2006 Benchmark Descriptions. SIGARCH Comput. Archit. News 34, 4 (Sept. 2006), 1--17."},{"key":"e_1_3_2_1_11_1","unstructured":"J. Howard etal 2021. fastai. https:\/\/github.com\/fastai\/fastai.  J. Howard et al. 2021. fastai. https:\/\/github.com\/fastai\/fastai."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Simon Kornblith etal 2019. Do Better ImageNet Models Transfer Better? arXiv:1805.08974 [cs.CV]  Simon Kornblith et al. 2019. Do Better ImageNet Models Transfer Better? arXiv:1805.08974 [cs.CV]","DOI":"10.1109\/CVPR.2019.00277"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066138"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2923577"},{"key":"e_1_3_2_1_15_1","unstructured":"H. Liu etal 2012. Feature selection for knowledge discovery and data mining. Vol. 454. Springer Science & Business Media.  H. Liu et al. 2012. Feature selection for knowledge discovery and data mining. Vol. 454. Springer Science & Business Media."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"H. M. Makrani etal 2021. Adaptive Performance Modeling of Data-Intensive Workloads for Resource Provisioning in Virtualized Environment. ACM ToMPECS 5 4 Article 18 (mar 2021) 24 pages.  H. M. Makrani et al. 2021. Adaptive Performance Modeling of Data-Intensive Workloads for Resource Provisioning in Virtualized Environment. ACM ToMPECS 5 4 Article 18 (mar 2021) 24 pages.","DOI":"10.1145\/3442696"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2015.7056070"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3195970.3196047"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8715080"},{"key":"e_1_3_2_1_21_1","volume-title":"Recent Advancements in Microarchitectural Security: Review of Machine Learning Countermeasures. In MWSCAS'20","author":"Sayadi H.","year":"2020","unstructured":"H. Sayadi 2020 . Recent Advancements in Microarchitectural Security: Review of Machine Learning Countermeasures. In MWSCAS'20 . 949--952. H. Sayadi et al. 2020. Recent Advancements in Microarchitectural Security: Review of Machine Learning Countermeasures. In MWSCAS'20. 949--952."},{"key":"e_1_3_2_1_22_1","volume-title":"StealthMiner: Specialized Time Series Machine Learning for Run-Time Stealthy Malware Detection Based on Microarchitectural Features. In GLSVLSI'20","author":"Sayadi H.","year":"2020","unstructured":"H. Sayadi 2020 . StealthMiner: Specialized Time Series Machine Learning for Run-Time Stealthy Malware Detection Based on Microarchitectural Features. In GLSVLSI'20 . 175--180. H. Sayadi et al. 2020. StealthMiner: Specialized Time Series Machine Learning for Run-Time Stealthy Malware Detection Based on Microarchitectural Features. In GLSVLSI'20. 175--180."},{"key":"e_1_3_2_1_23_1","volume-title":"On the Detection of Kernel-Level Rootkits Using Hardware Performance Counters. In ASIACCS'17","author":"Singh B.","year":"2017","unstructured":"B. Singh 2017 . On the Detection of Kernel-Level Rootkits Using Hardware Performance Counters. In ASIACCS'17 . 483--493. B. Singh et al. 2017. On the Detection of Kernel-Level Rootkits Using Hardware Performance Counters. In ASIACCS'17. 483--493."},{"key":"e_1_3_2_1_24_1","unstructured":"Baohua Sun etal 2019. SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data. arXiv:1903.06246 [cs.CV]  Baohua Sun et al. 2019. SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data. arXiv:1903.06246 [cs.CV]"},{"key":"e_1_3_2_1_25_1","volume-title":"ICANN","year":"2018","unstructured":"Chuanqi Tan et al. 2018. A Survey on Deep Transfer Learning . In ICANN 2018 , Vera Kurkov\u00e1 et al. (Eds.). Springer, Cham, 270--279. Chuanqi Tan et al. 2018. A Survey on Deep Transfer Learning. In ICANN 2018, Vera Kurkov\u00e1 et al. (Eds.). Springer, Cham, 270--279."},{"key":"e_1_3_2_1_26_1","volume-title":"Unsupervised Anomaly-Based Malware Detection Using Hardware Features. In RAID'14","author":"Tang A.","year":"2014","unstructured":"A. Tang 2014 . Unsupervised Anomaly-Based Malware Detection Using Hardware Features. In RAID'14 . Springer, 109--129. A. Tang et al. 2014. Unsupervised Anomaly-Based Malware Detection Using Hardware Features. In RAID'14. Springer, 109--129."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.23919\/DATE48585.2020.9116340"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196515"}],"event":{"name":"GLSVLSI '22: Great Lakes Symposium on VLSI 2022","location":"Irvine CA USA","acronym":"GLSVLSI '22","sponsor":["SIGDA ACM Special Interest Group on Design Automation"]},"container-title":["Proceedings of the Great Lakes Symposium on VLSI 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3526241.3530326","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3526241.3530326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:16Z","timestamp":1750186936000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3526241.3530326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":28,"alternative-id":["10.1145\/3526241.3530326","10.1145\/3526241"],"URL":"https:\/\/doi.org\/10.1145\/3526241.3530326","relation":{},"subject":[],"published":{"date-parts":[[2022,6,6]]},"assertion":[{"value":"2022-06-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}