Malicious codes are proliferating at an alarming rate. These days, malware is the preferred means of attack for criminals (attackers), as criminals launch attacks on computers. Malware assaults are usually carried out through the Internet via email, fraudulent websites, or downloaded packages. Numerous kinds of malicious codes exist, including worms, viruses, rootkits, trojan horses, cryptolocker malware, adware, etc. Despite the reality that there is no tested way to detect malicious code, adopting cloud environments might reveal to be a beneficial approach. Malicious code has advanced to a new generation that is more effective at finding vulnerabilities by implementing advanced obfuscation and packing tactics. As a result of these circumstances, it is practically hard to identify sophisticated malware by using more traditional detection methods. This article offers an in-depth analysis of current methods for detecting malware in the cloud, as well as an examination of the historical development of these approaches. Furthermore, this paper aims to raise awareness of the importance of cloud environments in safeguarding users' data from computer hackers. In this paper, we proposed, malware detection in the cloud environment by combining various approaches. We use “NSL-KDD and “ANN” malware dataset for evaluation. By comparing the outcomes of tests, we found that the proposed model improved accuracy while reducing data loss, which suggesting it is superior to alternative approaches