Malware (Malicious Software) has become one of the major threats to today's computing world. Although Antivirus programs provide primary line of defense and detect previously known malware, they, along with other detection mechanisms falling short of detecting present day new and unknown complex malware. In this work, a new approach to detect malware, which uses reverse engineering and machine learning techniques was proposed and implemented. While Reverse Engineering was used to analyze malware, genuine software and extract important features and construct datasets from those features, machine learning techniques were used to build classification models, which would classify a new executable as either malware or genuine software.