## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Security and Privacy
#### 4.5.2 Authentication, Access Control, and Encryption  
Techniques for access control assist in limiting network access to assets to just those individuals or equipment that are permitted. Applications can specify access restriction guidelines, which the controller translates into packet-forwarding rules to limit data based on the starting and ending addresses and interfaces. In comparison, encryption prevents unapproved individuals from accessing confidential information.  
A dynamic and dispersed permissioning framework built around blockchain with attribute-driven data encryption has been utilized to successfully tokenize apps in mixed IoT areas, where tokens serve as the currency of the blockchain while reinforcement learning is utilized to optimize the security policy [327]. Furthermore, work in [328] utilizes blockchain to create regulations regarding access for IoT devices (sensors) while offering a trackable policy management system to avoid the spreading of fake rules, in which Smart Contracts (SC) are used to create standalone, unchangeable, and provable guidelines in blockchain while machine learning is utilized to detect security attacks. Alternatively, a framework called DLACB achieves access control through asymmetric encryption and a certificate-based authentication protocol, and various transfers between controllers, apps, and switching devices are added to a Private Blockchain (PB) using the method of consensus, while deep learning is utilized to authenticate users and determine the access level for a given user [329]. Moreover, Decentralized Access Control (DAC) is implemented using the Ethereum blockchain for AI-driven knowledge-generating hospital networks in order to prevent unauthorized parties from modifying sensitive health records, where all transactions are recorded in the distributed ledger [330]. Additionally, a framework known as Smart Contract Data Trading (SC-DT) provides decentralized authentication and access control for data trading between data owners and data purchasers using smart contracts, where similarity learning is used to verify the data's availability as an administrator-assistant tool for network management [331]. Similarly, another framework known as MSecureChainemploys decentralized authentication and access control and federated learning-based intrusion detection in a metaverse context for KDN smart devices, which establish trustworthy connections for communication [332]. Likewise, an evidence management system known as SIEMF for the internet of vehicles leverages deep learning to predict incident modeling while using self-executing contracts and attribute-based encryption to authorize entry and generate operations for permissioning rules in cases where granular access control has been effective due to blockchain technology [333]. Moreover, in [334], for a Knowledge-Defined Internet of Health Things Network, Support Vector Machines (SVMs) are integrated with blockchain and self-executing contracts for secure user identification, access control, and threat detection in order to transmit data to healthcare applications.  
For performing authentication, authorization, and auditing in healthcare IoT networks, fuzzy logic has been used to derive knowledge on user behavior in achieving these tasks in a hyperledger blockchain framework called FBASHI [335]. Moreover, Neuro-Fuzzy (NF) machine learning and blockchain have been used in combination to provide privacy-preserving authentication, where the purpose of the neuro-fuzzy system is to recognize anomalous authentication inujries in vehicular networks, while blockchain is used for transactions and revocations [336].