MLTPED-BFC: Machine learning-based trust prediction for edge devices in the blockchain enabled fog computing environment

Abstract

The utilization of edge devices in fog computing services is increasing every day to achieve effective communication between edge devices as it reduces the latency and processing time. When the number of edge devices increases and operate in various applications, it is seen an increase in malfunctioning of devices due to compromises in security aspects. An increase in the number of un-trustworthy activities leads to loosing of end users to any service provider. So all edge devices must be labeled as trustworthy or not, based on their previous transactions, leading to effective communications. Finding and maintaining the trust score of edge devices is the most pressing concern in the distributed communication environment. Considering all the issues, this paper propose a Machine Learning-based Trust Prediction for Edge Devices in the Blockchain enabled Fog Computing Environment (MLTPED-BFC). The proposed scheme uses an ensemble of Support Vector Regression (SVR) and Multivariable Logistic Regression (MLR) for predicting the trust score of each edge device and updates it after every successful communication. The prediction and updating of the trust score is carried out by the fog server without any biasing. This Artificial Intelligence driven approach enhances communication effectiveness and security by classifying devices as trustworthy or not, improving the overall reliability of the distributed system. The proposed scheme is proved to be secured based on informal security analysis. Extensive simulations are carried out to validate the proposed scheme's effectiveness and compare it with existing schemes. The proposed MLTPED-BFC mechanism have attained 98.91% of accuracy, 0.0048 loss rate, 98.92% of precision, 98.32% of recall, 98.96% of F-Measure and took 356 s for 100 iterations.