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

Khan, Muhammad Sajjad; Gul, Noor; Kim, Junsu; Qureshi, Ijaz Mansoor; Kim, Sumin

doi:10.1155/2020/2509081

Cited by 18 publications

(9 citation statements)

References 31 publications

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“…In this configuration, malicious devices sending false information can cause the severe degradation of the performance of the network. Khan et al [117] proposed the mitigation to these attacks using GA-based soft decision fusion. This scheme achieved better performance and a lower probability of errors than conventional schemes.…”

Section: Evolutionary Techniques For Securitymentioning

confidence: 99%

IoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques

et al. 2021

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Internet of Things connects the physical and cybernetic world. As such, security issues of IoT devices are especially damaging and need to be addressed. In this treatise, we overview current security issues of IoT with the perspective of future threats. We identify three main trends that need to be specifically addressed: security issues of the integration of IoT with cloud and blockchains, the rapid changes in cryptography due to quantum computing, and finally the rise of artificial intelligence and evolution methods in the scope of security of IoT. We give an overview of the identified threats and propose solutions for securing the IoT in the future.

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Section: Evolutionary Techniques For Securitymentioning

confidence: 99%

IoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques

et al. 2021

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“…Influenced by certain motives, malicious users (MUs) intrude into the CSS networks. The security threats and protection of CSS from the malicious users (MUs) are currently the major concern for researchers [25][26][27]. The work in [28][29][30] examined defensive mechanisms to reduce the presence of Byzantine attacks, jamming attacks, and primary user emulation attackers (PUEA).…”

Section: Introductionmentioning

confidence: 99%

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Abbasi¹,

Jamil²,

Chen³

2022

Computers, Materials &Amp; Continua

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Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs' environment and when lower and higher SNRs information is carried by the different categories of MUs.

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“…Some heuristic approaches in CSS can lead to an optimal global decision. Among them, a genetic algorithm (GA), a class of computational algorithm motivated by evolution, is a good candidate to find the optimal solution by applying bioinspired approaches to given problems [17,18]. On the other hand, a machine learning (ML) technique is another good candidate by learning surrounding environments.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Gul

et al. 2020

Wireless Communications and Mobile Computing

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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.

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

Cited by 18 publications

References 31 publications

IoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques

IoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

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

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