Malware Detection based on Deep Learning

Malware detection is a crucial component of cybersecurity that seeks to locate and eliminate malicious software that is intended to damage or take advantage of any network or programmable device. Because modern malware is so complex and constantly changing, traditional malware detection methods like signature-based approaches are not up to the task. This study investigates how deep learning, a branch of artificial intelligence, can be used to improve malware detection. We can increase detection accuracy and adjust to new, unseen malware by utilizing deep learning models, which are capable of automatically learning and extracting features from data. This study examines various deep learning structures and strategies utilized in malware identification, assesses their efficacy, and delves into potential advancements and obstacles in the domain.

کلیدواژه ها:

Malware Detection ، deep learning ، cybersecurity