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Malicious software (malware) constitute one of the most pressing cyber threats intended to cripple critical infrastructure, render infected systems unusable, permanently erase data from storage systems. The number of malware has skyrocketed through the use of enormous malware development toolkit. Run-time analysis has recently been used to overcome the limitations of current detection engines due to code obfuscation techniques such as polymorphism and metamorphism. However run-time approaches face a critical challenge of processing a large number of run-time malware features which may fail to provide real time protection. In this paper, we propose a hybrid framework by using more than one complementary filters and a wrapper feature selection approach to identify the most significant run-time behavioural characteristics of malware. The novelty of the proposed framework is that it exploits the …