Utilizing GAN and Sequence Based LSTMs on Post-RF Metadata for Near Real Time Analysis
| dc.contributor.author | Barnes-Cook, Blake Alexander | en |
| dc.contributor.committeechair | Gerdes, Ryan M. | en |
| dc.contributor.committeechair | O'Shea, Timothy James | en |
| dc.contributor.committeemember | Chantem, Thidapat | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2023-01-18T09:00:34Z | en |
| dc.date.available | 2023-01-18T09:00:34Z | en |
| dc.date.issued | 2023-01-17 | en |
| dc.description.abstract | Wireless anomaly detection is a mature field with several unique solutions. This thesis aims to describe a novel way of detecting wireless anomalies using metadata analysis based methods. The metadata is processed and analyzed by a LSTM based Autoencoder and a LSTM based feature analyzer to produce a wide range of anomaly scores. The anomaly scores are then uploaded and analyzed to identify any anomalous fluctuations. An associated tool can also automatically download live data, train, test, and upload results to the Elasticsearch database. The overall method described is in sharp contrast to the more weathered solution of analyzing raw data from a Software Designed Radio, and has the potential to be scaled much more efficiently. | en |
| dc.description.abstractgeneral | Wireless communications are a major part of our world. Detecting unusual changes in the wireless spectrum is therefore a high priority in maintaining networks and more. This paper describes a method that allows centralized processing of wireless network output, allowing monitoring of several areas simultaneously. This is in sharp contrast to other methods which generally must be located near the area being monitored. In addition, this implementation has the capability to be scaled more efficiently as the hardware required to monitor is less costly than the hardware required to process wireless data. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35907 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/113213 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Signal Processing | en |
| dc.subject | Anomaly Detection | en |
| dc.subject | Elasticsearch | en |
| dc.subject | Omnisig | en |
| dc.subject | Sensing | en |
| dc.subject | Analytics | en |
| dc.subject | Edge Intelligence | en |
| dc.subject | Wireless Security | en |
| dc.title | Utilizing GAN and Sequence Based LSTMs on Post-RF Metadata for Near Real Time Analysis | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |