TY - GEN
T1 - Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks
AU - O'Reilly, Colin
AU - Gluhak, Alex
AU - Imran, Muhammad
AU - Rajasegarar, Sutharshan
PY - 2012
Y1 - 2012
N2 - Anomaly detection in a Wireless Sensor Network is an important aspect of data analysis in order to facilitate intrusion and event detection. A key challenge is creating optimal classifiers constructed from training sets in which the anomaly rates are varying due to the existence of non-stationary distributions in the data. In this paper we propose an adaptive algorithm that can dynamically adjust the anomaly rate parameter, which can be represented by a model parameter of a one-class quarter-sphere support vector machine. This algorithm operates in an online, iterative manner producing an optimal model for a training set, which is presented sequentially. Our evaluations demonstrate that our algorithm is capable of constructing optimal models for a training set that minimizes the error rate on the classification set compared to a static model, where the anomaly rate is kept stationary.
AB - Anomaly detection in a Wireless Sensor Network is an important aspect of data analysis in order to facilitate intrusion and event detection. A key challenge is creating optimal classifiers constructed from training sets in which the anomaly rates are varying due to the existence of non-stationary distributions in the data. In this paper we propose an adaptive algorithm that can dynamically adjust the anomaly rate parameter, which can be represented by a model parameter of a one-class quarter-sphere support vector machine. This algorithm operates in an online, iterative manner producing an optimal model for a training set, which is presented sequentially. Our evaluations demonstrate that our algorithm is capable of constructing optimal models for a training set that minimizes the error rate on the classification set compared to a static model, where the anomaly rate is kept stationary.
KW - Adaptive Models
KW - Anomaly Detection
KW - Concept Drift
KW - Non-Stationary Environment
KW - Security
KW - Wireless Sensor Network
UR - https://www.scopus.com/pages/publications/84867967519
U2 - 10.1109/SECON.2012.6275776
DO - 10.1109/SECON.2012.6275776
M3 - Conference contribution
AN - SCOPUS:84867967519
SN - 9781467319058
T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
SP - 191
EP - 199
BT - 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012
T2 - 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2012
Y2 - 18 June 2012 through 21 June 2012
ER -