Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily identifies the changes made in the data due to proper structure. The developed model supports privacy-preserving public auditing to provide a secure cloud storage system. The data owners upload the data in the cloud and edit the data using the private key. An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches. The data files requested by the data owner are audit by a third-party auditor and the multiowner authentication method is applied during the modification process to authenticate the user. The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2-167 ms when compared to the existing Advanced Encryption Standard and Blowfish.
Cyber-Physical System (CPS) is an integration of physical components like actuators, sensors and various types of equipment with the Internet possessing computational ability for efficient communication. A Heterogeneous Independent Network (HINT) is a realistic model that is used for the analysis of inter-dependability between the power grid and communications network. In the traditional Deep [Formula: see text]-Learning method, action needs to be stored in the [Formula: see text] table for the prediction. In real case studies, many state and action values affect the performance of the model. Existing Deep [Formula: see text]-Network (DQN) model generates all possible actions for the [Formula: see text]-values and this involves the generation of excessive information that causes the model to overfit. In this research, the Neural Network is applied to estimate the state–action in the DQN and to store the particular state–action value instead of storing all the state–action values as followed in the traditional method. The HINT model provides realistic failure propagation in the network and its state–action value overfits the existing DQN method due to the presence of more information. The proposed DQN with reinforcement learning stores selected state–action values in the [Formula: see text] tables and eliminates irrelevant information that helps to increase the accuracy and reduce the computational time. The DQN with reinforcement learning is applied to adaptively learn the system to select the optimal action in a continuous interaction with a stochastic environment. The proposed DQN model involves the application of reward function to store state–action value with higher probability based on prediction and eliminates other state–action values. Features such as intra-degree, inter-betweenness, substation-betweenness, relay-betweenness and feature vector are extracted and given as input to the DQN to characterize the critical nodes. The proposed DQN method is evaluated on the HINT network and synthetic network to analyze its efficiency in fault detection. The result shows that the HINT network has a lower prediction error compared to the existing Deep Neural Network (DNN) method. The proposed DQN and LSTM models have accuracies of 98% and 93% in fault prediction, respectively.
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