Contact Details

nameCarlos Garcia Cordero
positionPhD 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

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 cybersecurity, 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 honours.


Additional Attributes


ID2T - The Intrusion Detection Dataset Generation Toolkit

Carlos Garcia Cordero, Emmanouil Vasilomanolakis, Max Mühlhäuser
December 2017

HOLEG: a Simulator for Evaluating Resilient Energy Networks based on the Holon Analogy

Rolf Egert, Carlos Garcia Cordero, Andrea Tundis, Max Mühlhäuser
In: 21st IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017), October 2017

Evidence-Based Trust Mechanism Using Clustering Algorithms for Distributed Storage Systems

Giulia Traverso, Carlos Garcia Cordero, Mehrdad Nojoumian, Reza Azarderakhsh, Denise Demirel, Sheikh Mahbub Habib, Johannes Buchmann
In: 15th Annual Conference on Privacy, Security and Trust (PST), August 2017

Increasing the Resilience of Cyber Physical Systems in Smart Grid Environments using Dynamic Cells

Andrea Tundis, Carlos Garcia Cordero, Rolf Egert, Alfredo Garro, Max Mühlhäuser
In: ICCPS 2017: 19th International Conference on Cyber-Physical Systems, p. 796-807, 2017

Analyzing Flow-based Anomaly Intrusion Detection using Replicator Neural Networks

Carlos Garcia Cordero, Sascha Hauke, Max Mühlhäuser, Mathias Fischer
In: 14th Annual Conference on Privacy, Security and Trust (PST), p. 317 - 324, December 2016

On Probe-Response Attacks in Collaborative Intrusion Detection Systems

Emmanouil Vasilomanolakis, Michael Stahn, Carlos Garcia Cordero, Max Mühlhäuser
In: IEEE Conference on Communications and Network Security, p. 279 - 286, October 2016

Multi-stage Attack Detection and Signature Generation with ICS Honeypots

Emmanouil Vasilomanolakis, Shreyas Srinivasa, Carlos Garcia Cordero, Max Mühlhäuser
In: IEEE/IFIP Workshop on Security for Emerging Distributed Network Technologies (DISSECT), p. 1227 - 1232, April 2016

Towards the creation of synthetic, yet realistic, intrusion detection datasets <b>(best paper award)</b>

Emmanouil Vasilomanolakis, Carlos Garcia Cordero, Nikolay Milanov, Max Mühlhäuser
In: IEEE/IFIP Workshop on Security for Emerging Distributed Network Technologies (DISSECT), p. 1209 - 1214, April 2016

SkipMon: a Locality-Aware Collaborative Intrusion Detection System

Emmanouil Vasilomanolakis, Matthias Kruegl, Carlos Garcia Cordero, Mathias Fischer, Max Mühlhäuser
In: International Performance Computing and Communications Conference (IPCCC), p. 1 - 8, December 2015

Probe-response attacks on collaborative intrusion detection systems: effectiveness and countermeasures

Emmanouil Vasilomanolakis, Michael Stahn, Carlos Garcia Cordero, Max Mühlhäuser
In: IEEE Conference on Communications and Network Security (CNS), p. 699 - 700, September 2015


2 Entries found


Detecting Attacks, Intrusions and Anomalies in Smart Grids

Master Thesis


The power grid infrastructure is experiencing a dramatic change in the way it produces, distributes, and stores electricity. With these advancements, however, a new set of threats are also being enabled. In order to defend the smart grid infrastructure against novel attacks, new mechanisms for discovering threats must be developed. Fortunately, there is plenty of new information collected by intelligent sensors which can be leveraged to create mechanisms to detect attacks, intrusions and anomalies in smart grids.

With the addition of intelligent sensing devices, known as smart meters, information about usage patterns in the smart grid is being collected. This thesis project aims at developing intrusion detection techniques that can model normal usage patterns and detect deviations from these models. The developed techniques will rely on different machine learning algorithms and statistical analysis.

In order to evaluate methodologies for detecting threats in smart grids, we will provide real-world data related to the production and consumption of electricity, gas and heat in a real smart grid. Different machine learning algorithms need to be tested and evaluated on top of this data. Software is also expected to be developed where the proposed methodologies are demonstrated.



A Toolkit for Synthetic Injection of Attacks into Network Data

Bachelor Thesis


Nowadays Intrusion Detection Systems (IDS) have established as a mandatory line of defense in critical
networks. One main aspect during the development of a IDS is the evaluation and optimization of
the detection algorithms. Currently there is no standardized model for the evaluation of the detection
efficiency. A common approach has been the use of static datasets, but the publicly available datasets
have flaws in many regards, like their actuality, the absence of up-to-date attacks and the lack of realism
due to synthetically injected attacks. This makes it difficult to gather meaningful results and compare
algorithms with each other.

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