Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

Section: Related Workmentioning

confidence: 99%

“…where γ is the signal-to-noise ratio (SNR) of the received signal, and λ is the threshold, v is the product of time (T) and bandwidth (B), and Q u represents the marqum Q function. The cumulative probability of detection and false alarm at the FC are represented by Equations (8) and 9,…”

Section: System Modelmentioning

confidence: 99%

An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

Tephillah

Manickam

2020

“…In random opposite MU (ROMU), the MU randomly sends the inverse of the local sensing result to the FC. To mitigate the effect of these attacks, several different schemes were proposed [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Khan

Gul

et al. 2020

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

“…Various attacks which severely degrade the performance of the CSS have been studied in the CRN. e representative attacks are Byzantine users' attack, jamming attack, and primary user emulation attack (PUEA) [29][30][31][32][33]. e Byzantine users' attack is a type of spectrum sensing data falsification (SSDF) attack, where malicious users (MUs) report false information to the FC.…”

mentioning

confidence: 99%

“…In [30], the authors isolated SSDF outliers by utilizing Z-test; the SSDF attack is mitigated via q-out-of-m scheme. Similarly, in [31], the authors utilized a linear-weighted combination scheme to eliminate the effect of the SSDF attack on the final sensing decision. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious users from legitimate users.…”

mentioning

confidence: 99%

A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

Khan

Gul

Kim

et al. 2020

Internet of ings (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. erefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. e main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipathaffected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs' sensing information. e weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. rough extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.