arXiv subjects

Mikel Izal

Publications and source records attributed to Mikel Izal.

3 recordsLinked to original sources

arXiv2026

CTF as a Service: A reproducible and scalable infrastructure for cybersecurity training

Capture The Flag (CTF) competitions have established themselves as a highly effective pedagogical tool in cybersecurity education, offering students hands-on experience in realistic attack and defense scenarios. However, organizing and hosting these events requires considerable infrastructure effort, which frequently limits their adoption in academic settings. This paper presents the design, iterative development, and evaluation of a CTF as a Service (CaaS) platform built on Proxmox virtualization, leveraging Infrastructure as Code (IaC) tools such as Terraform and Ansible, container orchestration via Docker Swarm, and load balancing with HAProxy. The system supports both a development-centered workflow, in which challenges are automatically deployed from a Git repository through a CI/CD pipeline, and a deployment-oriented workflow for ad-hoc infrastructure provisioning. The paper describes the design decisions made, the challenges encountered during development, and the solutions implemented to achieve session persistence, external routing, and challenge replicability. The platform is designed to evolve into a CTF hosting service with commercial potential, and future lines of work are outlined regarding automatic scaling, monitoring integration, and frontend standardization.

cs.CR↗

arXiv2022

Crypto-ransomware detection using machine learning models in file-sharing network scenario with encrypted traffic

Ransomware is considered as a significant threat for most enterprises since the past few years. In scenarios wherein users can access all files on a shared server, one infected host can lock the access to all shared files. We propose a tool to detect ransomware infection based on file-sharing traffic analysis. The tool monitors the traffic exchanged between the clients and the file servers and using machine learning techniques it searches for patterns in the traffic that betray ransomware actions while reading and overwriting files. The proposal is designed to work for clear text and for encrypted file-sharing protocols. We compare three machine learning models and choose the best for validation. We train and test the detection model using more than 70 ransomware binaries from 26 different strains and more than 2500 hours of not infected traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries, including those not used in training phase (unseen). This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detected.

cs.CR↗

arXiv2013

Duplicate detection methodology for IP network traffic analysis

Network traffic monitoring systems have to deal with a challenging problem: the traffic capturing process almost invariably produces duplicate packets. In spite of this, and in contrast with other fields, there is no scientific literature addressing it. This paper establishes the theoretical background concerning data duplication in network traffic analysis: generating mechanisms, types of duplicates and their characteristics are described. On this basis, a duplicate detection and removal methodology is proposed. Moreover, an analytical and experimental study is presented, whose results provide a dimensioning rule for this methodology.

cs.NI↗