Learning from Others: User Anomaly Detection Using Anomalous Samples from Other Users

Machine learning is increasingly used as a key technique in solving many security problems such as botnet detection, transactional fraud, insider threat, etc. One of the key challenges to the widespread application of ML in security is the lack of labeled samples from real applications. For known or common attacks, labeled samples are available, and, therefore, supervised techniques such as multi-class classification can be used. However, in many security applications, it is difficult to obtain labeled samples as each attack can be unique. In order to detect novel, unseen attacks, researchers used unsupervised outlier detection or one-class classification approaches, where they treat existing samples as benign samples. These methods, however, yield high false positive rates, preventing their adoption in real applications.