An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

Abstract

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained. The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms. It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

“…According to the distance of evidence and the classical conflict coefficient, the new conflict coefficient is calculated, and the conflict weight of evidence is obtained. The fuzzy weight is calculated by fuzzy entropy, and the weight of credibility is obtained by updating credibility [35]. Our scheme also uses a weighted fusion approach, the difference is that we use Hidden Markov Model to determine the weight of nodes.…”

A Multisource Situation Information Fusion Method Based on Dynamic Evidence Combination

“…en, associations between alerts were established using the knowledge base to generate what were known as superalerts. e method proposed by Liu has poor fusion accuracy in a complex network environment, with large resource consumption and low fusion efficiency [12]. Zheng et al used the Pignistic distance to measure the similarity between different pieces of evidence based on sampling past evidence using a sliding window [13].…”

“…As an indeterminate reasoning theory [24], evidence theory was first proposed by Dempster and later improved by Shafer [12]. Evidence theory is an extension of probability theory.…”

“…In equation (12), if tc ij � 0, 1 ≤ i, j ≤ n, the similarity between the evidence displayed by the ith sensor and the jth sensor is low, that is, there is a conflict. If tc ij � 1, 1 ≤ i, j ≤ n, then the evidence submitted by the ith sensor and the jth sensor is in agreement.…”

“…. }; //initialize the alert records in time t (5) q � 1; //set the counter (6) While (q ≤ n) (7) GetAlert (Ak); //select the q th alert (8) d � Calculate S (); //calculate the similarity using equation ( 7)-( 11) (9) If (d > S) (10) S � d; (11) Else (12) q � q+1; (13) End if (14) End while (15) If (S < T) then ( 16)…”