Enhancing Microservice Security Through Ai-Supported Defense Strategies

Microservices Architecture (MSA) is a notable architectural style in software development because of its ability to decompose applications into independent services. This feature enables the development of scalable systems that optimize the use of cloud computing resources. Nevertheless, the development of software organized into multiple microservices can expand the attack surface and introduce new pathways for malicious activity. Thus, this paper presents a Systematic Mapping Study (SMS) based on 36 primary studies on the use of artificial intelligence-based defense strategies to support threat detection or mitigation in MSA. In short, this study aimed to identify the main application domains, attack types, defense strategies, artificial intelligence techniques, and datasets used in the proposed solutions. The results reveal that distributed learning techniques have not been thoroughly investigated, reinforcing the necessity to propose approaches capable of detecting malicious actions without significantly altering software behavior. Finally, this paper also discusses the main findings of this investigation.