Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Fast similarity search in the presence of longitudinal scaling in time series databases
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Towards correcting input data errors probabilistically using integrity constraints
MobiDE '06 Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access
Combining filtering and statistical methods for anomaly detection
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Sensitivity of PCA for traffic anomaly detection
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Sensor network data fault types
ACM Transactions on Sensor Networks (TOSN)
Sensor faults: Detection methods and prevalence in real-world datasets
ACM Transactions on Sensor Networks (TOSN)
Declarative support for sensor data cleaning
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
PAQ: time series forecasting for approximate query answering in sensor networks
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
Proceedings of the Fourth Symposium on Information and Communication Technology
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Wireless sensor systems aid scientific studies by instrumenting the real world and collecting measurements. Given the large volume of measurements collected by sensor systems, one problem arises-an automated approach to identifying the ''interesting'' parts of these datasets, or anomaly detection. A good anomaly detection methodology should be able to accurately identify many types of anomaly, be robust, require relatively few resources, and perform detection in (near) real time. Thus, in this paper, we focus on an approach to online anomaly detection in measurements collected by sensor systems, where our evaluation, using real-world datasets, shows that our approach is accurate (it detects over 90% of the anomalies with few false positives), works well over a range of parameter choices, and has a small (CPU, memory) footprint.