The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more depe...
As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. ...
Senaka Buthpitiya, Ying Zhang, Anind K. Dey, Marti...
Operators of 3G data networks need to distinguish the performance of each geographic area in their 3G networks to detect and resolve local network problems. This is because the qu...
Security analysis of learning algorithms is gaining increasing importance, especially since they have become target of deliberate obstruction in certain applications. Some securit...
Modern machine learning techniques provide robust approaches for data-driven modeling and critical information extraction, while human experts hold the advantage of possessing hig...
Anomaly detection is an important data mining task. Most existing methods treat anomalies as inconsistencies and spend the majority amount of time on modeling normal instances. A r...
One of the primary issues with traditional anomaly detection approaches is their inability to handle complex, structural data. One approach to this issue involves the detection of...
Abstract. Recent studies estimate that peer-to-peer (p2p) traffic comprises 40-70% of today's Internet traffic [1]. Surprisingly, the impact of p2p traffic on anomaly detectio...
Irfan Ul Haq, Sardar Ali, Hassan Khan, Syed Ali Kh...
Previous methods of network anomaly detection have focused on defining a temporal model of what is "normal," and flagging the "abnormal" activity that does not...
Kevin M. Carter, Richard Lippmann, Stephen W. Boye...
—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...