History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network

Abstract

In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

One‐to‐Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

“…The outcome shows that these MUs in CSS increase the false alarm and misdetection, resulting in an increased interference to the primary transmission and reduced throughput of the network. Simulations confirmed that the proposed one-to-many relation based KLD method leads to more accurate and sophisticated detection than the traditional soft combination schemes in [46,47].…”

“…In this paper, Kullback Leibler Divergence (KLD) has been employed to protect the CSS against the spectrum falsification attack (SFA) of always no (AN), always yes (AY), random opposite (RO), and the always opposite (AO) categories of MUs by assigning weights to the sensing reports of SUs before global combination at the FC. In our previous study [46], SUs perform their local sensing, report soft energies to the FC, and also store this information in its local database. FC determines the KL divergence score against each user and also acknowledges this same information to the user.…”

“…A normally declared user tries to send mean of the previous energy reports to the FC based on its current observation. The work in [46,47] uses the KLD to determine the probability distribution function (PDF) dissimilarity of a particular SU under the presence and absence hypothesis of the licensed user channel. The PDF uses the energy statistics of an individual user under both hypotheses for declaring it normal or malicious.…”

An Energy Efficient Cooperative Spectrum Sensing for Cognitive Radio-Internet of Things with Interference Constraints

“…As a result, based on the flexible sensing time slot, τ p s using the KLD award score, each CR-IoT user to sense the PU licensed spectrum more efficiently. Now, we can calculate the KLD award score, δ of the proposed ED method using the flexible sensing time slot, τ p s with interference constraints for scenario II which is defined as between the two normally distributed functions f (ψ) and f (ψ) [5,50,51,59] as follows:…”

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

“…In this paper, the CSS sensing performance is optimized in the presence of MUs reporting false information to the FC, by reducing miss detection and false alarm probabilities, resulting in overall reduction in error probability. In our previous study [30], SUs perform their local sensing and report soft energies to the FC and also store the information in their local database. After then, the FC determines the KL divergence score against each SU and also acknowledges this same information to the SU.…”