Elevating Security Measures in Cyber-Physical Systems: Deep Neural Network-Based Anomaly Detection with Ethereum Blockchain for Enhanced Data Integrity

Abstract

The rapid development of physical device-based data collection in emerging technology needs smart, secure, and intelligent transmission. Cyber physical systems compete with the requirement of intelligent transmission of data. In cyber physical systems, security is a very challenging task due to the heterogeneous connections of devices in real time. This paper proposes a novel methodology for cyber-attack finding in cyber physical systems. The proposed system employed a DNN-deep neural network for the categorization of normal and attack data. The employed deep neural network design for 4 hidden layers for the detection of anomalies. For the secured transmission, we employed the blockchain process in Ethereum. The process of Ethereum generates blocks of blockchain with headers and transmits data over the cyberworld to the physical world with the alteration of data. For the authentication of the projected algorithm tested on two real-time datasets, such as NSL-KDD15 and CIDDS_001. The working of proposed algorithm is very promising in compression of existing algorithms of deep learning like RNN-recurrent neural networks, DBN, and DNN.