Decentralised firewall for malware detection
This addresses cybersecurity threats for users by combining blockchain and neural networks, but it appears incremental as it applies existing methods to a new decentralized context.
The paper tackles malware detection by developing a decentralized firewall that uses a deep belief neural network to classify Portable Executable files as malicious or benign, achieving results validated on 4,000 files.
This paper describes the design and development of a decentralized firewall system powered by a novel malware detection engine. The firewall is built using blockchain technology. The detection engine aims to classify Portable Executable (PE) files as malicious or benign. File classification is carried out using a deep belief neural network (DBN) as the detection engine. Our approach is to model the files as grayscale images and use the DBN to classify those images into the aforementioned two classes. An extensive data set of 10,000 files is used to train the DBN. Validation is carried out using 4,000 files previously unexposed to the network. The final result of whether to allow or block a file is obtained by arriving at a proof of work based consensus in the blockchain network.