Cybersecurity threats are increasingly sophisticated and diverse, presenting significant challenges to organizations seeking to protect their networks and data. Traditional methods of threat detection, such as signature-based approaches, often struggle to keep up with the evolving nature of cyberattacks. As a result, there is a growing need for more adaptive, efficient, and scalable solutions. This paper explores the use of Deep Neural Networks (DNNs) in combination with event profiles for enhancing cybersecurity threat detection. By analyzing patterns of system events, network traffic, and user behaviors, DNNs can learn to identify anomalous activity that may indicate potential cyber threats.