Utilizing GAN and Sequence Based LSTMs on Post-RF Metadata for Near Real Time Analysis

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Barnes-Cook, Blake Alexander

Chairs dc:contributor.committeechair

• Gerdes, Ryan M.
• O'Shea, Timothy James

Committee member dc:contributor.committeemember

• Chantem, Thidapat

• Signal Processing
• Anomaly Detection
• Elasticsearch
• Omnisig
• Sensing
• Analytics
• Edge Intelligence
• Wireless Security

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35907

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/113213