%0 Journal Article %1 noauthororeditor %A Sharma, Shalini %A Hussain, Syed Zeeshan %D 2022 %J International Journal of Computer Networks & Communications (IJCNC) %K Algorithm Cloud Coefficient Entropy Firefly Machine Measure Optimization Support Trust Vector Weighted computing model %N 5 %P 117-132 %R 10.5121/ijcnc.2022.14508 %T Weighted Coefficient Firefly Optimization Algorithm and Support Vector Machine for Trust Model and Link Reliability %U https://aircconline.com/ijcnc/V14N5/14522cnc08.pdf %V 14 %X Cloud computing is widely used by organizations and individuals due to its flexibility and reliability. The trust model is important for cloud computing to detect malicious users and protect user privacy. The existing research faces the issues of local optima trap and overfitting problems when a training user node is idle for more time. This research proposed Weighted Coefficient Firefly Optimization Algorithm (WCFOA) with Support Vector Machine (SVM) for the trust model calculation and identifying paths with better Quality of Services (QoS). The weighted coefficient is added to the FOA model to balance the exploration and exploitation in the search of identifying optimal path based on reliability score. The WC-FOA method measures the link reliability in the model and SVM detects the malicious users in the model. The WC-FOA model selects the optimal path for transmission in terms of trust and efficient QoS parameters. The entropy measure and link reliability are provided as input to the SVM model for the detection of attacks in the network. The WCFOA-SVM model has 96% malicious user detection, whereas the Random Forest Hierarchical Ant Colony Optimization (RF-HEACO) has 92 % accuracy.

@article{noauthororeditor, abstract = {Cloud computing is widely used by organizations and individuals due to its flexibility and reliability. The trust model is important for cloud computing to detect malicious users and protect user privacy. The existing research faces the issues of local optima trap and overfitting problems when a training user node is idle for more time. This research proposed Weighted Coefficient Firefly Optimization Algorithm (WCFOA) with Support Vector Machine (SVM) for the trust model calculation and identifying paths with better Quality of Services (QoS). The weighted coefficient is added to the FOA model to balance the exploration and exploitation in the search of identifying optimal path based on reliability score. The WC-FOA method measures the link reliability in the model and SVM detects the malicious users in the model. The WC-FOA model selects the optimal path for transmission in terms of trust and efficient QoS parameters. The entropy measure and link reliability are provided as input to the SVM model for the detection of attacks in the network. The WCFOA-SVM model has 96% malicious user detection, whereas the Random Forest Hierarchical Ant Colony Optimization (RF-HEACO) has 92 % accuracy.}, added-at = {2022-11-23T07:37:42.000+0100}, author = {Sharma, Shalini and Hussain, Syed Zeeshan}, biburl = {https://www.bibsonomy.org/bibtex/25e2d81a309d26e44349d55be0410b206/laimbee}, doi = {10.5121/ijcnc.2022.14508}, interhash = {946d4283433a062ffa604db7b7b222e0}, intrahash = {5e2d81a309d26e44349d55be0410b206}, issn = {ISSN 0974 - 9322 (Online) ; 0975 - 2293 (Print)}, journal = {International Journal of Computer Networks & Communications (IJCNC)}, keywords = {Algorithm Cloud Coefficient Entropy Firefly Machine Measure Optimization Support Trust Vector Weighted computing model}, language = {English}, month = {September}, number = 5, pages = {117-132}, timestamp = {2022-11-23T07:37:42.000+0100}, title = {Weighted Coefficient Firefly Optimization Algorithm and Support Vector Machine for Trust Model and Link Reliability}, url = {https://aircconline.com/ijcnc/V14N5/14522cnc08.pdf}, volume = 14, year = 2022 }