As technology advances, there is an increase in devices connected to the internet. Malware infections are evolving in accordance with technological trends. Although the existing malware detection methods are advanced, there is a further need to increase its detection efficiency. Traditional detection techniques use emulated environments like virtual machines and sandboxes that lower malware’s ability to reveal its true behavior during run-time. The existing detection methods lack traceability. The source of malware in a targeted network remains unknown. Further, the existing malware detection techniques have a centralized database. Such databases serve as single points for various attacks. Research studies suggest blockchain technology as a probable solution to most of the lacunae in the existing malware detection techniques. Blockchain provides a ledger system that allows a record of all digital events. This provides traceability. Additionally, blockchain provides distributed decentralization that allows shared processing and updating of databases. This paper proposes a model that utilizes a trust model for malware detection using blockchain technology. This proposed model consists of two phases: The data Generation phase and the Consensus phase. In the data generation phase, blocks containing file data are created and after further analysis are added to the existing blockchain. In the consensus phase, the files are processed for malware detection. Additionally, the proposed model is enhanced with a trust model that is based on three parameters (1) Behaviour history (2) Risk associated with behaviour (3) Feedback of behaviour. The proposed work aims to enhance the overall performance of malware detection.