Miyaguchi–Preneel Snefru Cryptographic Blockchain and Maximum Likelihood Consensus Deep Convolutional Q-Learning for Secure Data Access in E-Learning

E-learning is a novel perception that includes all educational activities of an individual or group working online, synchronously or asynchronously, connecting via various devices through the internet. The e-learning system has faced some concerns related to security, availability and reliability. To overcome these challenges, an important security approach is required to preserve the data of the e-learning system. A novel Miyaguchi–Preneel Snefru Cryptographic Blockchain and Maximum Likelihood Consensus Deep Convolutional Q-Learning Network (MPSCB-MLCDCQN) is introduced with three different processes namely data collection, access control and data analysis. In the proposed MPSCB-MLCDCQN, Internet of Things (IoT) devices are employed to sense and collect student activities during the e-learning process. Secondly, secure data access is performed through the Miyaguchi–Preneel Snefru hashes decentralized blockchain technology for avoiding unauthorized access. Finally, the Maximum Likelihood Consensus Deep Convolutional Q-Learning Network (MLCDCQN) is applied to analyze the student data collected from the IoT devices to make optimal action. The student data are analyzed using the Maximum Likelihood Consensus regression function by learning the features of input data and predicting the student performance behavior with higher accuracy. A comprehensive experiment of the proposed MPSCB-MLCDCQN is conducted using e-learning activities' dataset in a CloudSim simulator with certain performance metrics such as confidentiality rate, data integrity rate, processing time and prediction accuracy with respect to a number of student data. The results discussed show that the MPSCB-MLCDCQN technique provides improved performance in terms of achieving higher security and data analysis than the existing methods.