This research investigates the application of machine learning in cybersecurity, aiming to address the significant gap in security personnel and the overwhelming volume of malware and cyber threats. With millions of new malware samples emerging every hour, traditional detection methods fall short, making the integration of machine learning an imperative advancement. This study illustrates how machine learning can enhance malware detection, recognize sophisticated cyber threats, and automate the response to security events, thereby alleviating the workload on cyber defense analysts. Through the development of a neural network model using TensorFlow, the research demonstrates a high accuracy rate (~99%) in classifying security alerts. The findings suggest that incorporating machine learning into Security Operations Centers (SOC) and Network Operations Centers (NOC) operations can significantly reduce analyst time by up to 78%, highlighting the potential of machine learning to transform cybersecurity practices.
