A hybrid model for detecting and preventing attacks on cloud computing systems using blockchain technology

Cloud computing is increasingly used for processing large volumes of data, particularly in businesses, but concerns about cloud security may hinder its broader adoption. Ensuring data confidentiality and protecting resources are critical aspects of cloud security. Numerous models, frameworks, techniques, and mechanisms have been proposed to detect and prevent attacks on cloud computing systems, mostly focusing on intrusion detection systems (IDSs) and intrusion prevention systems (IPSs). However, traditional IDSs and IPSs are inadequate for detecting and preventing various types of attacks on cloud systems. This study aims to review and analyze existing IDSs and IPSs for cloud computing systems and develop a novel model called the Hybrid Detecting and Preventing Model for Cloud Computing Systems (HDPMCC) using blockchain technology. Employing the design science method, the findings reveal that current IDSs and IPSs mainly address Distributed Denial-of-Service (DDOS) attacks. The proposed HDPMCC comprises five essential components: intrusion detection systems, behavioral analysis, blockchain technology, smart contracts, and privacy-enhancing technologies. Compared to existing IDSs and IPSs, HDPMCC offers a more effective approach for detecting and preventing attacks on cloud computing systems.