IPFS based storage Authentication and access control model with optimization enabled deep learning for intrusion detection

Abstract

Network security has benefited from intrusion detection, which may spot unexpected threats from network traffic. Modern methods for detecting network anomalies typically rely on conventional machine learning models. The human construction of traffic features that these systems mainly rely on, which is no longer relevant in the age of big data, results in relatively low accuracy and certain exceptional features. A storage authentication and access control model based on Interplanetary File System (IPFS) and a network intrusion detection system based on Chronological Anticorona Virus Optimization are hence the main goals of this research (CACVO-based DRN).The setup, user registration, initialization, data encryption and storage, authentication, testing, access control, and decryption stages are used here to perform the blockchain authentication and access control. After then, DRN is used to perform network intrusion detection. To do this, the recorded data log file is initially sent to the feature fusion module, which uses Deep Belief Network and hybrid correlation factors (DBN). After the feature fusion is complete, the proposed optimization technique, CACVO, which was recently developed by fusing the Chronological Concept with Anti Corona virus Optimization (ACVO) algorithm, is used to perform intrusion detection utilizing DRN. The experimental outcome shows that, based on the f-measure value of 0.939 and 0.938, respectively, the developed model achieved greater performance.