In this paper, we describe disparity, a tool that does parallel, scalable anomaly detection for clusters. Disparity uses basic statistical methods and scalable reduction operation...
The continuous evolution of the attacks against computer networks has given renewed strength to research on anomaly based Intrusion Detection Systems, capable of automatically dete...
The custom, ad hoc nature of web applications makes learning-based anomaly detection systems a suitable approach to provide early warning about the exploitation of novel vulnerabi...
William K. Robertson, Giovanni Vigna, Christopher ...
—The online detection of anomalies is a vital element of operations in data centers and in utility clouds like Amazon EC2. Given ever-increasing data center sizes coupled with th...
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...