Year: 2024
Type: article
Abstract: This paper proposes a comprehensive framework for real-time malware detection and monitoring tailored to operational systems. Leveraging advanced machine learning algorithms, our framework integrates continuous monitoring mechanisms to ensure timely detection and response to emerging threats. The framework emphasizes regular assessment of model performance using metrics such as the Population Stability Index (PSI), ensuring models remain effective and adaptive to evolving malware patterns. By deploying models within the production environment, the framework enables regular evaluation and adaptation, enhancing the robustness and reliability of the detection system. Our results demonstrate the framework’s efficacy in providing a scalable and efficient solution for real-time malware detection and monitoring, contributing to improved cybersecurity posture in dynamic and high-risk environments.
DOI: 10.59035/gljy1234
Author: Elshan Baghirov
Institution: Institute of Information Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404963888