Ensemble based categorization and adaptive model for malware detection

Malware, a term which was derived from two words; malicious software has caused many problem to the computer users throughout the world. Previously was known as many names; trojan, virus, worms, dialers and many others, thid potientially unwanted software simply labeled as malware. Malware is a software, which works as any other benigh software, but was designed to accomplish the goal of its writers. It was written to exploit the vulnerability of the target victim´s operating system or application. Previously was a primitive and easy to detect, it evolves to a sophisticated and professionally written piece of software. Current malware detection method involved string search algorithm which based on the pattern detection. This may include the use of signature based method. In this paper, we propose an ensemble categorization by using ensemble classification and clustering together with adaptive learning model.