Semi-supervised machine learning approach for unknown malicious software detection

Inductive bias represents an important factor in learning theory, as it can shape the generalization properties of a learning machine. This paper shows that biased regularization can be used as inductive bias to effectively tackle the semi-supervised classification problem. Thus, semi-supervised learning is formalized as a supervised learning problem biased by an unsupervised reference solution. The proposed framework has been tested on a malware-detection problem. Experimental results confirmed the effectiveness of the semi-supervised methodology presented in this paper.