| dc.description.abstract | Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life
and reshaped the technological world. Several tiny devices are seamlessly connected in
a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT
su ers from malicious attacks that pulverize communication and perturb network performance.
Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence
(AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate
CR-IoT networks to establish secure transmission against vicious attacks autonomously.
In this context, sub-band information from the Orthogonal Frequency Division Multiplexing
(OFDM) modulated transmission in the spectrum has been extracted from the
radio device receiver terminal, and a generalized state vector (GS) is formed containing
low dimension in-phase and quadrature components. Accordingly, a probabilistic method
based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission
with no abnormalities has been proposed to statistically model signal behaviors
inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF),
is implemented to perform state estimation and capture malicious attacks.
Subsequently, GS containing a higher number of subcarriers has been investigated. In
this connection, Variational autoencoders (VAE) is used as a deep learning technique
to extract features from high dimension radio signals into low dimension latent space
z, and DBN is learned based on GS containing latent space data. Afterward, to perform
state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump
Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that
appears due to either jammer attacks in transmission or cognitive devices in a network
experiencing di erent transmission sources that have not been observed previously. The
performance is assessed using the receiver o | en_US |
