{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:11:10Z","timestamp":1772118670122,"version":"3.50.1"},"reference-count":16,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The proliferation of unprotected Internet of Things (IoT) devices has been exponential in recent years, and it will continue to rise in the years to come owing to improvements in wireless connectivity. Due to its vulnerability to malware, reliable techniques for detecting IoT malware have become imperative. Problems with non\u2010independently and identically distributed data and poor generalizability nevertheless prevent us from reaching our objective. A methodical strategy for detecting malware is laid out in this study. Federated learning (FL) methods generate a notable degree of communication overhead given the high volumes of weights sent and received from the client\u2010side trained models. By combining the benefits of FL with Artificial Plant Optimization Algorithm (APO), this study intends to solve this problem. APO facilitated FL framework have been assessed applying it to benchmark malware datasets. In terms of effectiveness, reliability, scalability, generalizability, and communication efficiency, the APO facilitated FL has been experimentally evaluated on readily accessible malware datasets.<\/jats:p>","DOI":"10.1002\/itl2.70120","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T13:21:56Z","timestamp":1763472116000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimized Malware Detection Using Hybrid Federated\u2010APO Algorithm"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2069-6773","authenticated-orcid":false,"given":"Mohamed M.","family":"Abbassy","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence Beni\u2010Suef University  Beni\u2010Suef Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amr Ibrahim Awed","family":"El\u2010Shora","sequence":"additional","affiliation":[{"name":"Department of Computer Science Higher Institute of Management and Information Technology  Kafr El\u2010Sheikh Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayman Aboalndr Mohamed","family":"Aboalndr","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems Higher Institute of Management and Information Technology  Kafr El\u2010Sheikh Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3042174"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2963724"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3131981"},{"key":"e_1_2_8_5_1","first-page":"1273","volume-title":"Artificial Intelligence and Statistics, in: Proceedings of Machine Learning Research","author":"McMahan B.","year":"2017"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJWMC.2012.046787"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJCAT.2012.047162"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1166\/jctn.2012.2647"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.01.003"},{"key":"e_1_2_8_11_1","unstructured":"D.NoeverandS. E. M.Noever \u201cVirus\u2010MNIST: A Benchmark Malware Dataset \u201d(2021) arXiv:2103.00602."},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557533"},{"key":"e_1_2_8_13_1","unstructured":"R.Ronen M.Radu C.Feuerstein E.Yom\u2010Tov andM.Ahmadi \u201cMicrosoft Malware Classification Challenge \u201d(2018) arXiv:1802.10135."},{"key":"e_1_2_8_14_1","unstructured":"L.Lyu H.Yu X.Ma et al. \u201cPrivacy and Robustness in Federated Learning: Attacks and Defenses \u201d(2020) arXiv Preprint arXiv Preprint arXiv:2012.06337."},{"key":"e_1_2_8_15_1","first-page":"1467","volume-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning","author":"Biggio B.","year":"2012"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICICTA.2008.416"},{"key":"e_1_2_8_17_1","unstructured":"C.He A. D.Shah Z.Tang et al. \u201cFedCV: A Federated Learning Framework for Diverse Computer Vision Tasks \u201d(2021) arXiv preprint arXiv:2111.11066."}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70120","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70120","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T03:40:46Z","timestamp":1769139646000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/itl2.70120"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70120","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1002\/ITL2.70120\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v2\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v2\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1002\/ITL2.70120\/v1\/decision1","asserted-by":"object"}]},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]},"assertion":[{"value":"2025-05-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70120"}}