The growing sophistication of cyber threats, particularly malware, presents significant challenges for cybersecurity. Traditional malware detection methods often struggle to keep up with the evolving tactics and techniques employed by cybercriminals. This paper explores the potential of deep learning to enhance cybersecurity by providing robust and intelligent malware detection systems. Deep learning algorithms, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated superior performance in identifying and classifying complex malware patterns that traditional methods may miss.