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The term malware stands for malicious software. It is a program installed on a system without the knowledge of owner of the system. It is basically installed by the third party with the intention to steal some private data from the system or simply just to play pranks. This in turn threatens the computer's security, wherein computer are used by one's in day-today life as to deal with various necessities like education, communication, hospitals, banking, entertainment etc. Different traditional techniques are used to detect and defend these malwares like Antivirus Scanner (AVS), firewalls, etc. But today malware writers are one step forward towards then Malware detectors. Day-by-day they write new malwares, which become a great challenge for malware detectors. This paper focuses on basis study of malwares and various detection techniques which can be used to detect malwares.

with the fast and vast upliftment of IT sector in 21 st century, the question for system security also counts. As on one side, the IT field is growing with positivity, malware attacks are also arising on the other. Hence, a great challenge for zero day malware attack. Also, malware authors of metamorphic malware and polymorphic malware gain an extra advantage through mutation engine and virus generation toolkits as they can produce as many malware as they want. Our approach focuses on detection and classification of metamorphic malware according to their families. MM are hardest to detect by Antivirus Scanners because they differ structurally. We had gathered a total of 600 malware including those also that bypasses the AVS and 150 benign files. These files are disassembled, preprocessed, control flow graphs and API call graphs are generated. We had proposed an algorithm-Gourmand Feature Selection algorithm for selecting desired features from call graphs. Classification is done through WEKA tool, for which J-48 has given the most accuracy of 99.10%. Once the metamorphic malware are detected, they are classified according to their families using the histograms and Chi-square distance measurement formula.

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In this paper a shut circle control based buck -support