{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:13:11Z","timestamp":1791515591618,"version":"4.3.4"},"reference-count":144,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Defense","award":["MCS2106-23\u201301"],"award-info":[{"award-number":["MCS2106-23\u201301"]}]},{"name":"U.S. Department of Energy\/National Nuclear Security Administration","award":["DE-NA0004003"],"award-info":[{"award-number":["DE-NA0004003"]}]},{"name":"Lutcher Brown Distinguished Chair Professorship at The University of Texas at San Antonio"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber\u2013physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process\/file\/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan\/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost.<\/jats:p>","DOI":"10.3390\/s26061750","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T10:53:31Z","timestamp":1773140011000},"page":"1750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2285-0175","authenticated-orcid":false,"given":"Mazdak","family":"Maghanaki","sequence":"first","affiliation":[{"name":"Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soraya","family":"Keramati","sequence":"additional","affiliation":[{"name":"Department of Mineral, Metallurgical, and Materials Engineering, Universit\u00e9 Laval, Qu\u00e9bec City, QC G1V 0A6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7638-2186","authenticated-orcid":false,"given":"F. Frank","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Aerospace, and Industrial Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3147-739X","authenticated-orcid":false,"given":"Mohammad","family":"Shahin","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11416-009-0137-1","article-title":"A general definition of malware","volume":"6","author":"Kramer","year":"2010","journal-title":"J. Comput. Virol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, J., Kim, J., Jeong, H., and Lee, K. (2025). 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