Malware is refers to programs that purposely exploit computer systems' vulnerabilities for harmful purposes. It may be categorized by identifying whether it needs a host program to function and whether it makes copies of itself. Malware is an instance of malicious code with the purpose to disrupt the function of system and has potential to destruct a computer or network [1]. Nowadays, computer malware has become more sophisticated, using advanced code obfuscation technique to resist antivirus detection. Classification of malware samples plays an important part in building and maintaining security. The style of a malware classification system capable of supporting an oversized set of samples and adaptable to model changes at runtime is needed to spot the high range of malware variants. It is a supervised learning technique that created a proper model that may justify class and sort out information into correspondent options based. The advantage of classification is it acceptable of the many individuals since heap of researcher using classification. Next, classification method is quicker and it more accurate.
