I present RAACD, a software suite that detects misbehaving computers in large computing systems and presents information about those machines to the system administrator. I build this system using preexisting anomaly detection techniques. I evaluate my methods using simple synthesized data, real data containing coerced abnormal behavior, and real data containing naturally occurring abnormal behavior. I find that the system adequately detects abnormal behavior and significantly reduces the amount of uninteresting computer health data presented to a system administrator
In this dissertation, we examine the machine learning issues raised by the domain of anomaly detecti...
Anomaly detection is the task of identifying observations in a dataset that do not conform the expec...
The aim of this work is to create an application that allows modeling of user behavior and subsequen...
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems ...
An approach to the analysis of HAL/S software is discussed. The approach, called anomaly detection, ...
This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anom...
Anomaly detection is the problem of identifying data points or patterns that do not conform to norma...
Distributed, life-critical systems that bridge the gap between software and hardware are becoming a...
TINOpr oblems of importance in computer security are to I) detect the presence of an intruder masque...
We present an application of probabilistic approach to the anomaly detection (PAD). Byanalyzing sele...
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imp...
This work proposes anomalous computer system behavior detection method based on probabilistic automa...
The increasing complexity of modern high-performance computing (HPC) systems necessitates the introd...
Enterprise and high-performance computing systems are growing extremely large and complex, employing...
Networked computer systems continue to grow in scale and in the complexity of their components and i...
In this dissertation, we examine the machine learning issues raised by the domain of anomaly detecti...
Anomaly detection is the task of identifying observations in a dataset that do not conform the expec...
The aim of this work is to create an application that allows modeling of user behavior and subsequen...
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems ...
An approach to the analysis of HAL/S software is discussed. The approach, called anomaly detection, ...
This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anom...
Anomaly detection is the problem of identifying data points or patterns that do not conform to norma...
Distributed, life-critical systems that bridge the gap between software and hardware are becoming a...
TINOpr oblems of importance in computer security are to I) detect the presence of an intruder masque...
We present an application of probabilistic approach to the anomaly detection (PAD). Byanalyzing sele...
The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imp...
This work proposes anomalous computer system behavior detection method based on probabilistic automa...
The increasing complexity of modern high-performance computing (HPC) systems necessitates the introd...
Enterprise and high-performance computing systems are growing extremely large and complex, employing...
Networked computer systems continue to grow in scale and in the complexity of their components and i...
In this dissertation, we examine the machine learning issues raised by the domain of anomaly detecti...
Anomaly detection is the task of identifying observations in a dataset that do not conform the expec...
The aim of this work is to create an application that allows modeling of user behavior and subsequen...