Titulo Estágio
Intelligent Moving Target Defense for Intrusion Tolerance in microservice-based Systems
Local do Estágio
DEI
Enquadramento
Cyber-defensive mechanisms (e.g., firewalls and Intrusion Detection Systems) rely on static configurations. They cannot deal with the increasing complexity of cyber threats and attacks. Nowadays, attackers also apply Artificial Intelligence (AI) to boost the probability of attack success. Therefore, relying solely on static defenses to protect the system is obsolete.
Moving Target Defense (MTD) paradigm dynamically shifts the attack surface to confuse attackers and render the acquired knowledge useless. There are several ways to perform MTD, from shuffling IP addresses and network ports to applying dynamic changes on the software platform. One of the current research challenges in this topic is the adoption of AI techniques to empower MTD techniques (i.e., Intelligent MTD)
In recent years, the
deployment of software has experienced a shift from monolithic to modular architecture. In this scenario, the software components run as independent modules (i.e., microservices). Although the microservices architecture favors software modularity and maintenance, it also opens the door to other security vulnerabilities. The problem is how to protect the distributed components while assuring the system's dependability.
This work will propose intelligent MTD strategies for microservice protection (AI-based MTD for microservices). A realistic scenario (as in TeaStore - https://github.com/DescartesResearch/TeaStore), with system dependability and security evaluation, will be devised for this study.
Objetivo
The learning objectives of this master internship are:
1) Security, vulnerabilities: study the subject of software security and vulnerabilities;
2) Moving Target Defense technologies: understand concepts related to MTD, focusing on the application of shuffle, diversity, and redundancy techniques;
3) Microservices architectures: understand concepts associated with MTD, focusing on the application of shuffle, diversity, and redundancy techniques;
4) AI techniques: study AI/ML concepts, specifically Deep Reinforcement Learning; understand existing state-of-the-art AI-based MTD architectures;
5) Evaluating system dependability and security: deploying and evaluating an AI-based MTD for microservices protection.
6) Research Design: understand how to design and execute an experimental process to address complex and open research issues.
Plano de Trabalhos - Semestre 1
[09/09/2025 a 20/10/2025] Literature review
Study the concepts to be used in the internship, namely security, AI, MTD, and microservices.
[21/10/2025 a 05/11/2025] Analysis and selection of target techniques
Identification, analysis, and selection of which AI-based MTD techniques will be studied
[06/11/2025 a 03/12/2025] Definition of the experimental process
Design and plan the experimental process that will be used to conduct the study
[04/12/2025 a 15/01/2026] Write the dissertation plan
Plano de Trabalhos - Semestre 2
[06/02/2026 a 06/03/2026] Set up the experimental testbed
Set up the testbed required to conduct the experiments
[07/03/2026 a 17/04/2026] Conduct the experimental campaign
Use the testbed to conduct the experimental process
[18/04/2026 a 08/05/2026] Analyze, explore, and process the results
Process, explore, and analyze the results obtained from the experimental process on the use of AI-based MTD for microservices. Compare with existing results from the literature
[09/05/2026 a 05/06/2026] Write a scientific paper
[06/06/2026 a 08/07/2026] Write the thesis
Condições
This work occurs within the context of the NEXUS (C645112083-00000059 investment project no. .º 53) project and depending on the evolution of the internship a studentship may be available to support the development of the work. The work is to be executed at the laboratories of the CISUC’s Software and Systems Engineering (SSE) Group and Cyber Security Laboratory (CS-Lab).
Observações
Trabalho co-orientado pelo professor Matheus Torquato
Orientador
Joao Campos
jrcampos@dei.uc.pt 📩