{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:27:34Z","timestamp":1778167654092,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines (GBMs). The study provides insights into the effectiveness of memory-optimized machine learning solutions when detecting previously unseen malware. We found that ANNs shows the best performance with 93.44% accuracy classifying programs as either malware or legitimate even with extreme memory constraints.<\/jats:p>","DOI":"10.3390\/computers13030059","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T06:07:39Z","timestamp":1708668459000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Static Malware Analysis Using Low-Parameter Machine Learning Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Ryan","family":"Baker del Aguila","sequence":"first","affiliation":[{"name":"School of Engineering, Autonomous University of San Luis Potosi, Zona Universitaria, San Luis Potos\u00ed 78290, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlos Daniel","family":"Contreras P\u00e9rez","sequence":"additional","affiliation":[{"name":"School of Engineering, Autonomous University of San Luis Potosi, Zona Universitaria, San Luis Potos\u00ed 78290, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2419-8379","authenticated-orcid":false,"given":"Alejandra Guadalupe","family":"Silva-Trujillo","sequence":"additional","affiliation":[{"name":"School of Engineering, Autonomous University of San Luis Potosi, Zona Universitaria, San Luis Potos\u00ed 78290, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7566-0412","authenticated-orcid":false,"given":"Juan C.","family":"Cuevas-Tello","sequence":"additional","affiliation":[{"name":"School of Engineering, Autonomous University of San Luis Potosi, Zona Universitaria, San Luis Potos\u00ed 78290, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9633-3453","authenticated-orcid":false,"given":"Jose","family":"Nunez-Varela","sequence":"additional","affiliation":[{"name":"School of Engineering, Autonomous University of San Luis Potosi, Zona Universitaria, San Luis Potos\u00ed 78290, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15422","DOI":"10.1109\/JIOT.2021.3063840","article-title":"An evolutionary study of IoT malware","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2822877","article-title":"Evolution and Disruption in Network Processing for the Internet of Things: The Internet of Things (Ubiquity symposium)","volume":"2015","author":"Gregorio","year":"2015","journal-title":"Ubiquity"},{"key":"ref_3","first-page":"10","article-title":"Static malware analysis to identify ransomware properties","volume":"16","author":"Vidyarthi","year":"2019","journal-title":"Int. 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