  Open set recognition problems exist in many domains. For example in security,
new malware classes emerge regularly; therefore malware classification systems
need to identify instances from unknown classes in addition to discriminating
between known classes. In this paper we present a neural network based
representation for addressing the open set recognition problem. In this
representation instances from the same class are close to each other while
instances from different classes are further apart, resulting in statistically
significant improvement when compared to other approaches on three datasets
from two different domains.
