Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

From basic concepts in machine learning and data mining to advanced problems in the
machine learning domain, this book provides a unified reference for specific machine …

(57) ABSTRACT A method for profiling network traffic of a network. The method includes
obtaining a signature library comprising a plurality of signatures corresponding to a plurality …

Malware is one of the most damaging security threats facing the Internet today. Despite the
burgeoning literature, accurate detection of malware remains an elusive and challenging …

In general, in one aspect, the present invention relates to a method for profiling network
traffic of a network. The method includes obtaining a signature library comprising a plurality …

This work proposes a pattern mining approach to learn event detection models from
complex multivariate temporal data, such as electronic health records. We present recent …

Due to the growing threat of network attacks, the efficient detection as well as the network
abuse assessment are of paramount importance. In this respect, the Intrusion Detection …

Motivation Scientists increasingly rely on intelligent information systems to help them in their
daily tasks, in particular for managing research objects, like publications or datasets. The …

Various embodiments illustrated and described herein include systems, methods and
Software for content type classification. Some Such embodiments include determining a …