Nowadays, one of the most important threats that needs to be addressed is malware. Malicious programs have evolved over time, becoming more numerous and complex. Zero-day malwares are the new malware that are already widespread on the Internet but have not yet been identified. Traditional signature-based malware detection systems fail to detect these new malicious files because they have not yet been analyzed, so the systems will not have a valid signature with which to identify them and will cause false negatives when placed under examination. To identify and classify malware without the need of the malware signatures, I tried using different machine learning techniques to understand which algorithm was best suited for the task.