MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES

In this work, we present a flexible system that makes use of various machine learning methods to efficiently distinguish between malware and clean files while purposefully reducing false positives. In the field of cybersecurity, our strong framework is both flexible and strong, working along with different machine learning algorithms. Our study unfolds with an exploration of basic principles using the Random Model, K Nearest Neighbouring Classifier (KNN), and Logistic Regression as foundational parts, emphasizing the differentiation between malware and benign files. Extensive experiments on mediumsized datasets that include malware and clean files verify the effectiveness of our methodology. The system then goes through a painstaking scaling-up process that guarantees smooth operation with big datasets containing both malware and clean files. Our methodology is validated by analysing three important algorithms: Random Model, KNN, and Logistic Regression, each of which adds unique advantages to the malware detection system. The evaluation, which is carried out on several datasets, aims to minimize false positives while striking a compromise between precision and recall. Finally, our flexible system, implemented and evaluated on many datasets, demonstrates its efficacy in distinguishing malware from clean files. The framework’s flexibility and scalability make it an invaluable tool in the everevolving field of cybersecurity, providing a sophisticated method of malware detection. The proposed algorithms emphasize the framework’s potential as a supplementary tool to current cybersecurity measures while also adding to its reliability.