9 research outputs found
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
LTE Spectrum Sharing Research Testbed: Integrated Hardware, Software, Network and Data
This paper presents Virginia Tech's wireless testbed supporting research on
long-term evolution (LTE) signaling and radio frequency (RF) spectrum
coexistence. LTE is continuously refined and new features released. As the
communications contexts for LTE expand, new research problems arise and include
operation in harsh RF signaling environments and coexistence with other radios.
Our testbed provides an integrated research tool for investigating these and
other research problems; it allows analyzing the severity of the problem,
designing and rapidly prototyping solutions, and assessing them with
standard-compliant equipment and test procedures. The modular testbed
integrates general-purpose software-defined radio hardware, LTE-specific test
equipment, RF components, free open-source and commercial LTE software, a
configurable RF network and recorded radar waveform samples. It supports RF
channel emulated and over-the-air radiated modes. The testbed can be remotely
accessed and configured. An RF switching network allows for designing many
different experiments that can involve a variety of real and virtual radios
with support for multiple-input multiple-output (MIMO) antenna operation. We
present the testbed, the research it has enabled and some valuable lessons that
we learned and that may help designing, developing, and operating future
wireless testbeds.Comment: In Proceeding of the 10th ACM International Workshop on Wireless
Network Testbeds, Experimental Evaluation & Characterization (WiNTECH),
Snowbird, Utah, October 201