Published Paper Details:

Efficient detection of malware holds critical importance in cybersecurity, and this study explores the work of machine learning methodologies to enhance detection precision. By harnessing the power of logistic regression, random forests and decision trees Classifier algorithms, our methodology adeptly discerns among benign and malicious files based on meticulously extracted features. Employing rigorous feature selection techniques, we pinpoint the most discriminative attributes. Emphasizing the utilization of ensemble techniques and the interpretability of Decision Trees, our framework endeavors to furnish robust, comprehensible, and high-precision malware detection solutions. Through a meticulous comparative analysis, we meticulously scrutinize the strengths and limitations of each algorithm, empowering cybersecurity practitioners to make well-informed decisions. Additionally, we confront the challenge posed by imbalanced datasets, ubiquitous in real-world scenarios, ensuring our methodology maintains a high detection rate between benign and malicious samples.