Static Analysis Approach of Malware Using Machine Learning

The malware is continually changing in today’s Internet-dependent environment, with an estimated 560,000 new cases being produced each day. As a result, conventional detection technologies are no longer adequate, which presents a problem for security experts. Because it can learn and adapt to new threats, machine learning has become a potential method for enhancing security measures. This research suggests a machine learning-based approach for identifying different kinds of malware. The technology attempts to improve threat detection’s effectiveness and reactivity. A dataset of 96,724 malware samples is used to evaluate the suggested system, and several machine learning methodologies are contrasted. The outcomes show how machine learning may enhance security systems’ ability to combat continually changing malware threats.