Application of Hidden Markov Model for classifying metamorphic virus

Computer virus is a rapidly evolving threat to the computing community. These viruses fall into different categories. It is generally believed that metamorphic viruses are extremely difficult to detect. Metamorphic virus generating kits are readily available using which potentially dangerous viruses can be created with very little knowledge or skill. Classification of computer virus is very important for effective defection of any malware using anti virus software. It is also necessary for building and applying right software patch to overcome the security vulnerability. Recent research work on Hidden Markov Model(HMM) analysis has shown that it is more effective tool than other techniques like machine learning in detecting of computer viruses and their classification. In this paper, we present a classification technique based on Hidden Markov Model for computer virus classification. We trained multiple HMMs with 500 malware files belonging to different virus families as well as compilers. Once trained the model was used to classify new malware of its kind efficiently.