… Considering the generality of misuse detection-based … in unsupervised anomaly detection
techniques. In previous research [20], we have proposed an unsupervised anomaly detection …

… In fact, many practical anomaly detection problems often require a preprocessing in order
to … to be point anomaly detection problems, such that no further preprocessing is necessary …

… a completely unsupervised anomaly detection approach (RUAD) that exploits the fact that the
anomalies … In future works, we will further explore the problem of anomaly detection in HPC …

… Unfortunately, our anomaly detection module produces high positive rate (more than 20%)
for all four clustering algorithms. Therefore, our future work will be focused in reducing the …

… the application of unsupervised machine learning to building models of CPSs for anomaly
detection. … This is desirable, because for most realistic situations, we do not have data with real …

… Abstract Most current intrusion detection systems employ … geometric framework for
unsupervised anomaly detection, which … more easily formalize the problem of unsupervised …

… System have made it feasible to develop data-driven approaches to anomaly detection.
Compared with supervised analytics, unsupervised anomaly detection is more practical in …

… Based on assumptions that abnormal samples have different distributions, more promising
results for anomaly detection are yielded. Based on distribution-augmented contrastive …

… -box approach that does not require expert knowledge for configuration; and yet can still
address dimensionality and correlation challenges found in most anomaly detection systems. …

… algorithm is more accurate … of unsupervised systems. Some attacks (eg, DoS) produce a
large number of connections, which may undermine an unsupervised anomaly detection system…