AIDS-Based Cyber Threat Detection Framework for Secure Cloud-Native Microservices

Cloud-native architectures continue to redefine application development and deployment by offering enhanced scalability, performance, and resource efficiency. However, they present significant security challenges, particularly in securing inter-container communication and mitigating Distributed Denial of Service (DDoS) attacks in containerized microservices. This study proposes an Artificial Intelligence Intrusion Detection System (AIDS)-based cyber threat detection solution to address these critical security challenges inherent in cloud-native environments. By leveraging a Resilient Backpropagation Neural Network (RBN), the proposed solution enhances system security and resilience by effectively detecting and mitigating DDoS attacks in real time in both the network and application layers. The solution incorporates an Inter-Container Communication Bridge (ICCB) to ensure secure communication between containers. It also employs advanced technologies such as eXpress Data Path (XDP) and the Extended Berkeley Packet Filter (eBPF) for high-performance and low-latency security enforcement, thereby overcoming the limitations of existing research. This approach provides robust protection against evolving security threats while maintaining the dynamic scalability and efficiency of cloud-native architectures. Furthermore, the system enhances operational continuity through proactive monitoring and dynamic adaptability, ensuring effective protection against evolving threats while preserving the inherent scalability and efficiency of cloud-native environments.