IoT-based blockchain intrusion detection using optimized recurrent neural network

【Author】 Saravanan, V.; Madiajagan, M.; Rafee, Shaik Mohammad; Sanju, P.; Rehman, Tasneem Bano; Pattanaik, Balachandra

【Source】MULTIMEDIA TOOLS AND APPLICATIONS

【影响因子】2.577

【Abstract】In recent years, Intrusion Detection Systems (IDS) monitor the computer network system by collecting and analyzing data or information by identifying the behavior of the user or predicting the attacks by the automatic response. So, in this paper, the Blockchain-based African Buffalo (BbAB) scheme with Recurrent Neural Network (RNN) model is proposed for detecting the intrusion by enhancing security. Furthermore, normal and malware user datasets are collected and trained in the system and the dataset is encrypted using Identity Based Encryption (IBE). The encrypted data are securely stored in the blockchain in the cloud. Hereafter, Recurrent Neural Network (RNN) was employed to detect the intrusion in a cloud environment. African buffalo optimization was used in the RNN prediction phase for continuous monitoring of intrusion. Finally, the performance results of the developed technique are compared with other conventional models in terms of accuracy, precision, recall, F1-score, and detection rate. The outperformance of the designed model attains better accuracy of 99.87% and high recall of 99.92%.it shows the efficiency of the designed model to protect data and security in cloud computing.

【Keywords】Cloud computing; Intrusion detection system; Deep learning; Identity-based encryption; African buffalo optimization; Blockchain

【发表时间】2023 2023 SEP 16

【收录时间】2023-10-21

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-物联网