In the current age of interconnectedness, individuals leverage the benefits of communication and collaboration, however, this interconnectedness also exposes systems to an escalating array of cybersecurity threats, posing challenges to security and integrity. This project addresses this concern by concentrating on the development of a defense system that leverages advanced deep-learning techniques. The primary objective is to accurately detect intrusions in real-time, classify malware. Going beyond conventional cybersecurity measures, this defense system integrates deep learning methods, ensuring flexibility and responsiveness to counter evolving tactics employed by malicious entities. The defense system encompasses both Packet Files and Executable Files, significantly augmenting cybersecurity capabilities. To seamlessly align with established practices, LSTM, GRU, and RNN Models are employed for both Packet Files and Executable Files. The harmonious integration of these technologies with existing infrastructure is imperative for cultivating a secure digital environment.