No abstract

Centralized big data is an emerging field that manages information in one common repository. Currently, Internet of Things (IoT) devices are increasing massively for sensing and transmitting data to the big data environment. This information is sensitive and easily traced by attackers due to the centralized data management. To address the security and privacy issues and avoid the system in a single point of failure, in this paper the researchers proposed a new model, namely, BOBS CRABID i.e. Blockchain Based Secure Centralized Big Data model using distributed computing approach. In big data, Hadoop MapReduce is one of the distributed computing approaches which handles all information in a distributed manner. The BOBS CRABID model is designed by three ideas as Multi-Factor authentication, IoT Devices Data Collection and Processing, and Optics based MapReduce for Data Clustering. In multi-factor authentication, a lightweight camellia key generation algorithm (LCKGA) is used for their authenticating all devices based on ID, IP address, MAC and PUF. These credentials are saved in Blockchain Security Entity for authentication verification. All transactions are hashed and stored in the blockchain using Keccak hash algorithm which performs better than the traditional hashing (SHA) 2. Then the researchers conducted IoT devices data collection and pre-processing using Cross Correlation and Min-Max Normalization for redundant data pruning and normalization, respectively. Finally, clustering is performed in a distributed way for optimizing the storage and scalability performance. For that MapReduce is used in which OPTICS algorithm is applied for clustering based on the data size, type of context and nature of data (sensitive or non-sensitive). In this way, data is stored in the big data environment. Experiments were conducted for the proposed BOBS CRABID model and compared with the well-known methods using Hadoop 2.7.2. The proposed BOBS-CRABID model achieves better performance in terms of throughput (increase 1500mbps), response time (reduce 150ms), energy consumption (reduce 35%), attack detection rate (increase 30%), accuracy (increase 20%), computation overhead (300 kb), and time reduce 4s).