Giovani Rimon Abuaitah

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Recent real-world sensor network deployments have helped decision makers and sensor data analysts draw highly precise conclusions by relying on finer-grained raw sensory data. A small number of anomalous sensor readings, however, lead to misinterpretations and false conclusions. Hence, detecting and eliminating anomalies in sensor network deployments play a(More)
The performance of anomaly detection algorithms is usually measured using the total residual error. This error metric is calculated by comparing the labels assigned by the detection algorithm against a reference ground truth. Obtaining a highly expressive ground truth is by itself a challenging task, if not infeasible. Often, a dataset is manually labeled(More)
Modeling faults and malicious activities in sensor networks can be challenging. Designing and re-evaluating a “good” classifier to detect abnormalities imposes yet another challenge once the sensor network is deployed in the field. Common approaches among researchers involve obtaining publicly accessible labeled datasets, generating synthetic sensor data,(More)
In this paper, we propose an online practical anomaly detection framework rooted in machine learning to identify data-centric anomalies in sensor network deployments. The framework enables application administrators to train a network of deployed sensors, instructs the nodes to extract online statistical features, and allows every node in the network to(More)
Wireless sensor networks (WSNs) as an emerging technology faces numerous challenges. Sensor nodes are usually resource constrained. Sensor nodes are also vulnerable to physical attacks or node compromises. Answering queries over data is one of the basic functionalities of WSNs. Both resource constraints and security issues make designing mechanisms for data(More)
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