Numerical Simulation and Assessment of Hyper Parameter Tuned Machine Learning Based Malware Detection System

Abstract

In the realm of cybersecurity, the detection and mitigation of malware remain paramount challenges due to the constant evolution and sophistication of malicious software. This study presents a comprehensive numerical simulation and assessment of a hyperparameter-tuned machine learning (ML) system designed for the detection of malware. By employing a variety of ML algorithms, including decision trees, support vector machines, and neural networks, this research focuses on optimizing each model's hyperparameters to enhance detection accuracy. The methodology involves a rigorous simulation environment where numerous malware signatures and behaviors are analyzed to test the efficacy of the ML models. Hyperparameter tuning is achieved through advanced techniques such as grid search and randomized search, ensuring that each model operates at its optimal capacity. The results demonstrate a significant improvement in detection rates compared to traditional, non-tuned systems, with the tuned models achieving higher precision and recall metrics. This paper not only highlights the critical role of hyperparameter optimization in malware detection systems but also sets a benchmark for future research in employing machine learning to combat increasingly complex cybersecurity threats. The findings underscore the potential of hyperparameter-tuned ML models as robust tools in the ongoing battle against malware..