Analysis of Securing Edge-Cloud Computing and Network Based Deep Neural Intrusion Detection System as a Solution Model

Edge cloud technologies have transformed the way enterprises handle data processing, disaster recovery, and data protection. Moving computing resources closer to the network’s edge enables the edge cloud to deliver faster processing, lower latency, reliable data recovery and backups, enhanced scalability, and optimized bandwidth utilization. However, in comparison to traditional data-centers, this transition poses additional issues. A comparative analysis of business demands is required to guarantee that edge cloud computing meets specific criteria such as high performance, data protection, and data security. Nonetheless, there are challenges relating to disaster data recovery, network infrastructure, and standardization that must be overcome in order to fully exploit the potential of the edge cloud. In this context, the role of edge cloud solutions in disaster response and emergency management systems, as well as their impact on data backup, resource flexibility, and risk mitigation, requires further investigation. To ensure data security in edge cloud technology, blockchain plays a vital role; however, this architecture has limitations, specifically in disaster recovery scenarios. To address the limitations of blockchain architecture in edge cloud computing and to maintain data privacy and security in the edge cloud, we propose integrating a deep neural network (DNN) based intrusion detection system (IDS) model into the edge cloud architecture. This solution aims to enhance the security of the edge cloud while mitigating latency issues caused by blockchain, particularly in environments with high data recovery and emergency management requirements. We assessed our proposed DNN based IDS model using a publicly available intrusion dataset. Result shows that integrating DNN-based IDS into the edge cloud improves latency issues arising from the blockchain architecture during the data recovery and emergency management scenario.