Designing of blockchain-based cyber security for the protection of Distributed Denial of Service (DDoS) attacks on client–server networks

Abstract The complexity and difficulty of the ongoing and unstoppable cybercrimes in the traditional or conventional Artificial Intelligence (AI) system create the worst problems for the recent day’s modern cyberspace. The traditional systems are always dependent on centralized systems, which have insufficient performance to prevent and reveal DDoS attacks and modern cybersecurity challenges. The traditional systems of artificial intelligence (AI), machine learning (ML), and Deep Learning (DL) are limited only to the task of detecting DDoS attacks; they are not able to protect the client-server network cyberspace from DDoS attacks. Centralized-based property of the traditional systems produces a particular failure that makes them more attractive victims in cyberspace by DDoS attack criminals. The difficulty of deploying Artificial Intelligence (AI) cybersecurity techniques often results in poor DDoS attack protection performance, leaving organizations vulnerable and susceptible to DDoS attacks. The principal and major goal of the study is to formulate a strong Blockchain Technology-based Cyber Security emerging technology-oriented technique, particularly designing and modeling for protecting client-server network cyberspace from DDoS-based cyber-attacks. We have a few numbers of system methodologies like data collection (i.e, related literature review, dataset selection and collection, and experimental data collection), selection of performance evaluation parameters, validation process, experimental equations of performance evaluation metrics, and selection and collection of hardware and software resources. Different models are trained and tested on the CIC-DDoS2019 dataset; we have seen that there are different values of performance evaluation metrics for different models. The CNN model showed the value of 98.5% and 99.5% DDoS attack detection accuracy and prevention capability, respectively.