Intrusion Detection and Tolerance for Microservice Applications

Security is of paramount importance in the development of computer systems within our increasingly interconnected and computational world. This paper addresses the security challenges posed by microservices, a popular technology for developing computer systems. Microservices offer numerous advantages but also introduce new security concerns, given their greater attack surface and complex network activity. To address these challenges, the research explores the potential of Machine Learning (ML) techniques for intrusion detection and intrusion tolerance in microservice architectures. The main objective is to propose more effective approaches to secure microservice architectures using ML techniques for intrusion detection and intrusion tolerance. The research evaluates how ML techniques, particularly anomaly detection, can be tailored to detect intrusions in microservices-based systems and minimize false alarms. The contributions of this Ph.D. work include proposing ML-based intrusion detection approaches, providing design guidelines for intrusion detection systems, devising pre and post-processing techniques for efficiency, and researching intrusion tolerance solutions to overcome microservice challenges.