The advent of Artificial Intelligence (AI) and data science with Machine Learning (ML) and deep learning techniques has paved way for solving many real world problems. Malware detection is one such problem that is solved with AI based solutions. Malicious code that comes through genuine software components or through storage media and networks is termed as malware. This paper has made a reviews of literature for ascertaining the current academic thinking and the methods being used or methods possible to detect malware automatically. The study in this paper is divided into three categories known as ML methods for malware detection, deep learning methods for malware detection and optimization methods for improving malware prediction performance. Each category is summarized to have most useful insights. It covers malware research with various datasets available including Android apps based datasets. It has brief discussion on ML and deep learning methods along with their methodology. The insights of this paper provide good understanding on different methods existing, their approach, datasets collected and used besides evaluation metrics. These insights along with the proposed framework and experimental results can trigger further research with specific possibilities in future. Since the dataset utilized in this paper has more number of attributes the survey has identified that deep learning based approaches has more efficiency rather than the ML approaches. The integration of deep learning approaches with optimization techniques has enhanced the utilization of resources.

Malicious Code Detection, Malware Detection, Machine Learning, Deep Learning, Malware Detection Optimization