Decentralised firewall for malware detection

TL;DR

This paper presents a decentralized blockchain-based firewall system utilizing a deep belief neural network to classify PE files as malicious or benign, enhancing malware detection through innovative modeling and consensus mechanisms.

Contribution

It introduces a novel decentralized firewall architecture with a blockchain-powered malware detection engine using image-based deep learning for PE file classification.

Findings

High accuracy in malware classification

Effective blockchain consensus for decision making

Validated on extensive dataset

Abstract

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.