Giovani Rimon Abuaitah

Learn More
—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(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)
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)
High reprogramming latency/overhead A need for selective reprogramming (faulty/compromised nodes) SNMiner Approach: Facilitates fault modeling (direct interaction with the deployed sensor network) Simulates malicious activities within a real-world deployment Avoids network reprogramming (no interruption of operation) Constantly re-evaluates classification(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)
  • 1