| Title |
Blockchain-based model for incentivized cyber threat intelligence sharing / |
| Authors |
Venčkauskas, Algimantas ; Jusas, Vacius ; Barisas, Dominykas ; Misnevs, Boriss |
| DOI |
10.3390/app14166872 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2024, vol. 14, iss. 16, art. no. 6872, p. 1-20.. ISSN 2076-3417 |
| Keywords [eng] |
blockchain ; cyber threat intelligence ; hyperledger fabric ; incentive system ; IPFS |
| Abstract [eng] |
Sharing cyber threat intelligence (CTI) can significantly improve the security of information technology (IT) in organizations. However, stakeholders and practitioners are not keen on sharing CTI data due to the risk of exposing their private data and possibly losing value as an organization on the market. We present a model for CTI data sharing that maintains trust and confidentiality and incentivizes the sharing process. The novelty of the proposed model is that it combines two incentive mechanisms: money and reputation. The reputation incentive is important for ensuring trust in the shared CTI data. The monetary incentive is important for motivating the sharing and consumption of CTI data. The incentives are based on a subscription fee and a reward score for activities performed by a user. User activities are considered in the following three fields: producing CTI data, consuming CTI data, and reviewing CTI data. Each instance of user activity is rewarded with a score, and this score generates some value for reputation. An algorithm is proposed for assigning reward scores and for recording the accumulated reputation of the user. This model is implemented on the Hyperledger Fabric blockchain and the Interplanetary File System for storing data off-chain. The implemented prototype demonstrates the feasibility of the proposed model. The provided simulation shows that the selected values and the proposed algorithm used to calculate the reward scores are in accordance with economic laws. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
