Malware detection is a critical component of cybersecurity, aimed at identifying malicious software that can compromise systems, steal data, or disrupt operations. With the increasing sophistication of malware, traditional detection methods, such as signature-based approaches, are becoming less effective. Deep learning offers powerful tools for malware detection, leveraging vast amounts of data to identify complex patterns and behaviors associated with malicious software.This series of PhD projects will explore innovative methodologies and applications of deep learning for malware detection. The focus will be on developing robust systems capable of detecting both known and unknown malware types, enhancing accuracy and speed, and adapting to the rapidly evolving landscape of cyber threats. By focusing on various aspects of malware detection, such as behavioral analysis, adversarial robustness, and explainability, this research will contribute to the development of more effective and adaptive detection systems. The emphasis on practical applications alongside theoretical advancements will prepare researchers to make significant contributions to the field of cybersecurity and protect against emerging threats.