Evolutionary Binary Classification Using Cuckoo Search for Malware Perception in API Call Sequences

Abstract

Malware threats are continuing to grow in volume and sophistication. Current anti-virus software is ineffective on the new generation malware threats. Ongoing developments in machine learning models pose a promising alternative to act against virus attacks including detection of zeroday virus attacks. Some of the contemporary literature has explained the possibility of implementing machine learning algorithms to virus detection. Majority of these algorithms use n-gram characteristics of the dot EXE file code, where n is fixed to constant value. This manuscript proposes an algorithm to machine learning, whose learning and detection is also n-gram characteristics, however, the value of n is dynamic. In particular, the contribution of the manuscript is an Evolutionary Binary Classification that is built using cuckoo search (EBC-CS). Results from the execution of the program indicate a strong discrepancy between malicious software and benign software. The changes identified in classifier performance are evaluated in accordance with variations in malware prevalence.