# Blockchain-Based Model for Incentivized Cyber Threat Intelligence Sharing
## 4 Experimental Evaluation and Discussion
### Results and Discussion  
The key features of our innovation that distinguish it from other research in the field of incentivized CTI data sharing are as follows: the Hyperledger Fabric blockchain implementation, off-chain IPFS storage, a new incentive model, an algorithm for calculating reward scores, and the implementation of TLP for sensitive CTI data sharing. Ethereum and Hyperledger Fabric are two open-access core platforms for developing applications on blockchains [34]. The features of Hyperledger Fabric blockchain, which are consistent with the task solved, are as follows:  
* Hyperledger Fabric is a permissioned blockchain that enables the achievement of confidentiality and privacy goals through its design. * Hyperledger Fabric supports channel-based privacy that is important for the implementation of the TLP. This feature is unique in comparison to Ethereum blockchain. * Hyperledger Fabric does not require the use of digital currency to operate. This feature is unique in comparison to Ethereum blockchain. * Smart contracts, called chaincodes in Hyperledger Fabric, are developed using well-known popular programming languages such as Golang, Java, and Node.js. There is no need to learn a specific language, as is the case for Ethereum. * Hyperledger Fabric enables the low latency of transaction confirmation, as the confirmation can be acknowledged within only a few organizations [35]. The attractive and desirable features of Hyperledger Fabric have also been acknowledged by other researchers [14; 16; 19; 21] in the field of incentivized CTI data sharing. The next feature of the current innovation is off-chain IPFS storage. Shared CTI data can occupy a large volume; however, blockchains have limited data storage capacity. One of the recognized alternatives to expand blockchain data storage is the use of the IPFS [36]. The IPFS is an open-source data storage system that stores data in a distributed manner using key-value data access. The file is split into chunks after being uploaded into the IPFS. Each chunk is represented by a content-related hash key. The chunks are related to each other, and the hash key of the root chunk is stored in a blockchain ledger. The IPFS is a public-nature network. Therefore, any participant node can download the data from the IPFS. To ensure confidentiality, the data must be encrypted before being uploaded. We used the AES 256 algorithm to encrypt the data. Only two research works [15; 19] from those reviewed used the IPFS to store data off-chain. [PERSON] et al. [15] used the AES 256 algorithm to encrypt data, as we did in our case. In addition, AES symmetric keys were encrypted using the RSA public crypto system to share the keys over the network, as the network used was public. [PERSON] et al. [19] did not specify a particular encryption algorithm. However, again, the encryption doubled despite the Hyperledger Fabric blockchain being used. The symmetric keys are encrypted using an unspecified public crypto system. This double encryption is related to the CTI data verification process. The next feature of our innovation is the new incentive model. Many authors [11; 12; 13; 14; 15; 16; 17; 18; 19; 21; 23] have used incentives to either enhance trust in CTI data or motivate sharing, but they never pursued both goals together. However, [PERSON] et al. [22] noticed that the CTI data-sharing model must include economic incentives and maintain reputation management at the same time. We followed the observation of [PERSON] et al. [22] and proposed a new incentive model that combines economic incentives and reputation management. The economic incentives are based on a subscription fee. The use of a subscription fee was also proposed by [PERSON] et al. [19], although it was used quite differently, as a discount was given to the verifiers and contributors towards the next subscription. First, [PERSON] et al. [19] did not elaborate on the amount of the subscription fee. Second, they did not provide details of the amount of discount.