BDMDNA : Design of an efficient Blockchain-based Deep learning model for identification and mitigation of dynamic network attacks

Abstract

The increasing number of cyber-attacks on network systems has become a significant challenge for network security under real-time network scenarios. Deep learning models have proven to be effective in identifying network attacks. However, these models require a large amount of data for training, and their implementation can be computationally expensive when deployed on large-scale networks. To overcome these issues, this paper proposes a blockchain-based deep learning model that utilizes the advantages of blockchain to enhance the efficiency and security of network attack identification and mitigation. The proposed model uses a novel Proof-of-Wireless-Trust (PoWT) consensus algorithm to validate and secure the training data, and a customized Binary Cascaded Deep Learning Model (BCDLM) for training the model w.r.t. multiple attack signatures. The blockchain-based model is designed to detect and mitigate dynamic network attacks in real-time, thereby enhancing the security of network systems. The proposed model is evaluated using different network datasets.