Resilient Back Propagation Neural Network Security Model For Containerized Cloud Computing

Abstract

Cloud-native computing is getting more and more popular in recent years where containerized microservices architectural designs play a central role in building a distributed systems and services. On one hand, they bring convenience and simplicity to build massively scalable distributed cloud-native applications and enable continuous development and delivery for their services. On the other hand, they widen the surface of malicious intrusions, which, in turn, without proper defense mechanisms, lessens their benefits to a certain degree. Among the biggest threats of malicious intrusions are those that belong to the Distributed Denial of Service (DDoS) family. Such type of attacks are challenging because DDoS attacks are elevated hard-to-absorbed threats and have a high degree of variability in types, design, and complexity. In this work, resilient backpropagation neural network was used to build an intelligent network intrusion detection model against the most modernistic DDoS attacks in the cloud-native computing environment. We evaluated our proposed model using the benchmarking Canadian Institute for Cybersecurity evaluation CICDDoS 2019 dataset. Our proposed detection model has achieved high reflective DDoS attack detection. Therefore, it is appropriate to defend against reflective DDoS attacks in containerized cloud-native platforms. Experimental results indicate that the DDoS attack detection accuracy of the proposed resilient neural network model is as high as 97.07% which outperforms most of the well-known learning models mentioned in the most related work. Moreover, the proposed model has achieved a competitive run time performance that highly meets the delay requirements of containerized cloud computing.