Lessening Spectrum Sensing Data Falsification Attack by Weighted Fuzzy Clustering Means Using Simulation Annealing in Cognitive Radio Networks

Thulasimani, L.; Magdalene, A. Hyils Sharon

doi:10.1007/978-981-19-1111-8_33

Cited by 1 publication

(1 citation statement)

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“…Table 1 lists the results for various cluster sizes. Then, in terms of time and memory requirements, the proposed system is compared with FCM [38] and WFCM [39] and the Boost Graph Convolutional Network Clustering employed in previous work. According to a graph drawn for time against cluster count (Figures 7 and 8), the process takes less time to complete the more clusters there are.…”

Section: Resultsmentioning

confidence: 99%

Big Data Privacy Protection and Security Provisions of the Healthcare SecPri-BGMPOP Method in a Cloud Environment

Kuttiyappan,

Appadurai,

Kavin

et al. 2024

Mathematics

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One of the industries with the fastest rate of growth is healthcare, and this industry’s enormous amount of data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. However, maintaining privacy and security in an untrusted green cloud environment is difficult, so the data owner should have complete data control. A new work, SecPri-BGMPOP (Security and Privacy of BoostGraph Convolutional Network-Pinpointing-Optimization Performance), is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, was first applied to the input dataset to begin the clustering process. Second, it was enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplifying leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid Fragment Horde Bland Lobo Optimisation (HFHBLO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise it without risking their privacy or security.

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Section: Resultsmentioning

confidence: 99%