Binarized Spiking Neural Network with blockchain based intrusion detection framework for enhancing privacy and security in cloud computing environment

Abstract

Cloud computing (CC) is prone to attacks, which upsurges complication and erudition. By this, its origins provocative implications can enterprise data veracity, concealment, and accessibility. To overwhelm these issues, a Binarized Spiking Neural Network with block chain based deputized proof of stake consensus (DPoS) algorithm fostered intrusion detection scheme is proposed in this manuscript to enhance the privacy and the security on the cloud computing Environment (EP-DPoSBC-ES-BS4NN-IDS-CC). The data is amassed from NSL-KDD benchmark dataset. The first-level privacy process is carried with block chain based deputized proof of stake consensus (DPoS) algorithm. The secondary level privacy process is carried out by utilizing the pre-processing and the feature selection process. For, pre-processing, proposed Basic interlude gradient filtering (BIGF) are utilized to eradicate the unsolicited content and filtering pertinent data. The pre-processing outcome is supplied to the feature selection phase. Here, the ideal features are taken with the help of Weightiness espoused feature assortment approaches (WEFA). The data is classified as normal or abnormal based on Binarized Spiking Neural Network. Subsequently, the proposed EP-DPoSBC-ES-BS4NN-IDS-CC is examined under some performance metrics. The proposed technique attains 12.94 %, 17.68 %, 17.99 % and 13.96 % improved accuracy; 59.9 %, 50 %, 31.45 % and 48.17 % lower Computation Time and 3.19 %, 0.83 %, 2.1 % and 5.43 % higher AUC than the existing methods. Customers and cloud service providers may find this framework useful as a decision-support tool in helping them move their data in a safe, timely, and reliable manner. In future work, a prototype of the approach will develop in real-world scenario, capably inside a tight network of connected computers. It allows evaluate effectively in real-world utility.