Malware attacks are increasing rapidly as the technology continues to become prevalent. These attacks have become extremely difficult to detect as they continuously change their mechanism for exploitation of vulnerabilities in software. The conventional approaches to malware detection become inefective due to a large number of varying patterns and sequences, thereby requiring artificial intelligence-based approaches for the detection of malware attacks. In this paper, we propose an artificial neural networkbased model for malware detection. Our proposed model is generic as it can be applied to multiple datasets. We have compared our model with diferent machine-learning approaches. The experimentation results show that the proposed model can outperform other well-known approach as it achieves 99.6% , 98.9% and 99.9% accuracy on the Windows API call dataset, Top PE Imports Dataset and Malware Dataset, respectively.