Blockchain-Integrated Malware Detection Systems: Enhancing Accuracy and Trust in Cybersecurity

The increasing sophistication of malware requires sophisticated and reliable detection systems to protect critical infrastructure and sensitive data. The issues of accuracy and trust, often prevalent in traditional malware detection approaches due to centralized data management and vulnerability to tampered data, can thus be addressed. This paper proposes a malware detection system that integrates with Blockchain, increasing accuracy and adding a trust factor to the security system. For the malware detection process, the system executes a deep-learning model capable of obtaining high accuracy and success with a large dataset. Smart contracts are designed to implement decision-making in a transparent and immutable manner based on the validity of detection results. Utilizing data hashing, the system safely stores the detection results on the Blockchain, providing an auditable trail for evidence generation. The system adopts a PBFT consensus mechanism with high scalability and efficiency, guaranteeing rapid agreement between all blockchain nodes. It utilizes Polygon L2 zk-rollup (zkValidium) blockchain for high throughput and cost-effectiveness. The deep learning-based detection model is connected to the blockchain nodes using API integrations to enable seamless real-time communication. This paper presents the architecture implementation and the flow for detecting malware, providing an efficient, decentralized approach to detecting malware. By merging these two technologies, we expect to improve cybersecurity solutions' precision, scalability, and reliability and resolve data integrity and real-time validation issues.