| Autor: |
Ryan Baker del Aguila, Carlos Daniel Contreras Pérez, Alejandra Guadalupe Silva-Trujillo, Juan C. Cuevas-Tello, Jose Nunez-Varela |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Computers, Vol 13, Iss 3, p 59 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2073-431X |
| DOI: |
10.3390/computers13030059 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
