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Malware is any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new malware. Supervised machine-learning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect unknown malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.