Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System

Abstract

Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user's performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.

Delegation Based User Authentication Framework over Cognitive Radio Networks

“…When emulating a PU, a malicious entity can reduce the availability of spectrum for SUs. The misbehaving nodes can be categorized as [19,[30][31][32][33][34][35] -Selfish nodes: They seek to maximize their own gains at the expense of others. -Malicious nodes: They act to degrade the system or individual node performance with no explicit intention to maximize their own gains and act as a PU and transmit false information to the SU.…”

“…An attack is considered strong if it involves a minimal number of adversaries performing minimal operations but causing maximum damage or loss to the PUs and or SUs in the network. A brief summary of these attacks is given in Table 1 [19,[30][31][32][33][34][35][36][37][38][39][40][41][42]. Table 1.…”

“…Biased utility attack [33,34] Asynchronous sensing attack [35][36][37][38] Byzantine attack [39] False feedback attack [33] Selfish misbehaviour attack [39,40] SU tweaks parameters of the utility function Network Endo-parasite attack [33,34] Channel Ecto-parasite attack [33,34] Low cost ripple effect attack [33,34] SU attempts to increase the interference Key depletion attack [33,34] High round trip times and frequent retransmissions increase key usage Any attacks against confidentiality, integrity, authentication, non-repudiation, access control, and availability [19,33] Any attacks on below layers from application layer impact adversely on the layer Jellyfish attack [41] Routing information jamming attack [42] Performed in a network layer but affected to the other layers…”

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

“…The latter feature is available to users who sign up for biometry usage. Biometry saves the user the stress of regular logins into gadgets and wares (e.g., keys or cards) with privacy and identity theft challenges [8]. For instance, lost or forgotten login details can be accessed by a third party and be used illegally.…”

Activated carbon clogging analysis in an integration of constructed wetland with microbial fuel cell

“…Constructed wetland (CW) has been proven as a promising technology to treat wastewater pollution, and it has been successfully applied to secondary processing treatments of domestic sewage, leachate, rainfall runoff, and industrial effluent [1,2]. The regular CW is constructed by artificially constructing a pool or a grooved structure, filling in a certain depth of the filler layer, planting aquatic plants, and purifying the sewage by using the triple synergy of the physical, chemical and biological aspects of the substrate, plants and microorganisms.…”