Cybersecurity attacks are rising both in rate and complexity over time. More development and constant improvement in defensive approaches are needed to secure the operational systems against such attacks. Several malicious attacks pose severe security threats to organizations and users in today's internet age. It is vital to train enhanced malware classification systems to capture the variation in the malware type that belongs to the same family type. In this chapter, the author addresses the malware detection issue using a learning-based approach. First, the author explains various machine learning and deep learning algorithms to solve the problem. Next, the author provides practical implementation by proposing a deep learning-based framework on the open-source benchmark dataset on API calls. The dataset contains API calls during normal and malware-infected processes. The proposed framework trains a hybrid model of convolution neural network followed by long short-term memory to have a high malware detection rate.
