Title |
A Novel ORLLTMLP-Based Attack Detection and Blockchain-Aware Security Framework Using LCTFA in Smart City Applications |
ID_Doc |
38730 |
Authors |
Anbalagan, VY; Rajasekaran, S; Rajeeve, TD |
Title |
A Novel ORLLTMLP-Based Attack Detection and Blockchain-Aware Security Framework Using LCTFA in Smart City Applications |
Year |
2023 |
Published |
New Generation Computing, 41, 2 |
DOI |
10.1007/s00354-023-00210-9 |
Abstract |
ProblemSmart cities have become the mainstream of urbanization with the advancement of the internet of things (IoT). Utilizing the Internet, smart devices are permitted by the IoT networks to gather and process data among the smart city infrastructure. Therefore, faster adaptions of smart cities were limited by challenges like centralization, security, privacy, transparency, scalability, and verifiability.MotivationA novel ORLLTMLP-based attack detection and blockchain-aware security framework using LCTFA in smart city applications were proposed in this study. The major motivation of this research is to perform efficient attack detection and secure data storage using the blockchain and DL approaches, which comprehensively pondered the security and efficiency of data sharing.MethodsIn this model, the sensed data's dimensionality is diminished using MRC-LDA. Then, the attack detection and secure storage of data are carried out using ORLLTMLP and LCTFA, respectively.ResultsFinally, the experiential analysis reveals that an accuracy of 97.5% was accomplished by the proposed model.ImplicationsThe experiential outcomes exposed that the proposed scheme is efficient among the prevailing techniques for secure and efficient data sharing. |
Author Keywords |
Blockchain; Mutual range coefficient induced linear discriminant analysis (MRC-LDA); Sigmoid polynomial kernel adapted maximum relevance and minimum redundancy (SPKmRMR); Optimized rectified linear log tan multi-layer perceptron (ORLLTMLP); Modified logistic chaotic mapping optimized two fish algorithm (LCTFA) |
Index Keywords |
Index Keywords |
Document Type |
Other |
Open Access |
Open Access |
Source |
Science Citation Index Expanded (SCI-EXPANDED) |
EID |
WOS:000952770600001 |
WoS Category |
Computer Science, Hardware & Architecture; Computer Science, Theory & Methods |
Research Area |
Computer Science |
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