Contact Details

nameCarlos Garcia Cordero
positionPhD at GRK Privacy and Trust for mobile Users
email

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
Germany

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.

Publications

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

Author Giulia Traverso, Carlos Garcia Cordero, Mehrdad Nojoumian, Reza Azarderakhsh, Denise Demirel, Sheikh Mahbub Habib, Johannes Buchmann
Date August 2017
Kind Inproceedings
Book title15th Annual Conference on Privacy, Security and Trust (PST)
NoteTo appear.
LocationCalgary, CA
KeyTUD-CS-2017-0237
Research Areas CRISP, CYSEC, Telecooperation, SPIN: Smart Protection in Infrastructures and Networks, Theoretische Informatik - Kryptographie und Computeralgebra
Abstract In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes possible to detect unreliable evidence and establish countermeasures in order to discourage the collusion of storage servers. Furthermore, this trust mechanism is applied to the social secret sharing protocol AS3, showing that this new evidence-based trust mechanism enhances the protection of the stored documents.
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Theses

1 Entries found


On the Analysis & Generation of Synthetic Attacks for Intrusion Detection Systems

Master Thesis

finished


Intrusion Detection Systems (IDS) have established themselves as a mandatory line of defense for critical infrastructure. One main aspect during the development of an 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 and the absence of up-to-date attacks.This creates challenges in terms of the reproducibility and the comparison of results.


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