| Title | Anomaly Detection in RFID Networks Using Bayesian Blocks and DBSCAN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Prodanoff, Zornitza Genova, Penkunas, Andrew, Kreidl, Patrick |
| Conference Name | 2020 SoutheastCon |
| Date Published | March 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-6861-6 |
| Keywords | Adaptation models, anomaly detection, anomaly/intrusion detection, Bayes methods, Bayesian Blocks, Computational modeling, Human Behavior, IoT, NFC, pubcrawl, radio frequency identification, Radio frequency identification (RFID), radiofrequency identification, resilience, Resiliency, RFID, RFID security, RFIDs, security, traffic characterization, Two dimensional displays, Voronoi cells |
| Abstract | The use of modeling techniques such as Knuth's Rule or Bayesian Blocks for the purposes of real-time traffic characterization in RFID networks has been proposed already. This study examines the applicability of using Voronoi polygon maps or alternatively, DBSCAN clustering, as initial density estimation techniques when computing 2-Dimentional Bayesian Blocks models of RFID traffic. Our results are useful for the purposes of extending the constant-piecewise adaptation of Bayesian Blocks into 2D piecewise models for the purposes of more precise detection of anomalies in RFID traffic based on multiple log features such as command type, location, UID values, security support, etc. Automatic anomaly detection of RFID networks is an essential first step in the implementation of intrusion detection as well as a timely response to equipment malfunction such as tag hardware failure. |
| URL | https://ieeexplore.ieee.org/document/9249740 |
| DOI | 10.1109/SoutheastCon44009.2020.9249740 |
| Citation Key | prodanoff_anomaly_2020 |
Anomaly Detection in RFID Networks Using Bayesian Blocks and DBSCAN