Contact Details

nameCarlos Garcia Cordero
positionResearcher at GRK: Privacy and Trust for mobile Users

garcia (AT) tk(DOT)tu-darmstadt(DOT)de

phone+49 (6151) 16 - 23205
fax+49 (6151) 16 - 23202
officeS2|02 A 316
postal addressTU Darmstadt - FB 20
FG Telekooperation
Hochschulstraße 10
D-64289 Darmstadt

Research Interests

  • Machine learning

    • Anomaly Detection
    • Bayesian Networks
    • Deep Learning

  • Network Intrusion Detection

    • Collaborative Intrusion Detection
    • Distributed Intrusion Detection

  • Human Computer Interaction

    • 3D printing, computer graphics and 3D modeling tools

Short Biography

Carlos García Cordero is a scientist, systems engineer, mathematician, musician and thinker.

Carlos' research experience and interests are wide and cover diverse topics such as cyber-security, artificial intelligence, programming languages, compilers, machine learning and computer graphics, among others. 

Carlos is currently studying a PhD in Cyber Security and Distributed Machine Learning at TU Darmstadt. He has an MSc in Artificial Intelligence from The University of Edinburgh and a BSc in Computer Systems Engineering from the ITESM CSF in Mexico, both achieved with the highest honors.


Analyzing Flow-based Anomaly Intrusion Detection using Replicator Neural Networks

Author Carlos Garcia Cordero, Sascha Hauke, Max Mühlhäuser, Mathias Fischer
Date December 2016
Kind Inproceedings
Book title14th Annual Conference on Privacy, Security and Trust (PST)
JournalPrivacy, Security and Trust Conference
Pages317 - 324
Research Areas CASED, CRISP, Telecooperation, CYSEC, - SSI - Area Secure Smart Infrastructures, Fachbereich Informatik
Abstract Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.
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2 Entries found


Optimizing holon-based energy networks using Particle Swarm Optimization

Bachelor Thesis

in progress


Predicting vulnerabilities in software

Master Thesis

in progress

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