Training fuzzy deep neural network with honey badger algorithm for intrusion detection in cloud environment

Cloud computing (CC) has become one of the prominent technologies because of the significant utility services, which focus on outsourcing data to companies and individual clients. Intrusion Detection Systems (IDS) can be considered an effective solution to achieve security in the cloud computing environment. Blockchain and intrusion detection can be integrated to accomplish security and privacy in the cloud infrastructure. This research develops a new fuzzy deep neural network (FDNN) with Honey Bader Algorithm (HBA) for privacy-preserving intrusion detection technique, named FDNN-HBAID for cloud environment. The presented FDNN-HBAID system is based on the design of an intrusion detection approach with a blockchain-enabled privacy-preserving scheme. An effective training strategy with the FDNN model is applied for intrusion detection and classification. Moreover, FDNN-HBAID provides maximal-security resistance to alleviate zero-day vulnerability and guarantees integrity throughout the nodes and data confidentiality and authentication. In addition, the training process of the FDNN model is carried out using the HBA for optimal adjustment of the hyperparameters. Besides, the privacy-preserving blockchain and intelligent contract model is designed using the Ethereum library to offer privacy to the distributed IDS engine. The experimental validation on benchmark datasets revealed that the FDNN-HBAID approach had shown the potential to achieve security and privacy in the cloud infrastructure.