We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model ...
In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automat...
Current techniques for the formal modeling analysis of DoS attacks do not adequately deal with amplification attacks that may target a complex distributed system as a whole rather ...
Warehouse automation has progressed at a rapid pace over the last decade. While the tendency has been to implement fully automated solutions, most warehouses today exist as a mixtu...
Autonomic computer systems aim to reduce the configuration, operational, and maintenance costs of distributed applications by enabling them to self-manage, self-heal, self-optimiz...
Jules White, Douglas C. Schmidt, Aniruddha S. Gokh...