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

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

Author Emmanouil Vasilomanolakis, Carlos Garcia Cordero, Nikolay Milanov, Max Mühlhäuser
Date April 2016
Kind Inproceedings
PublisherIEEE
Book titleIEEE/IFIP Workshop on Security for Emerging Distributed Network Technologies (DISSECT)
Pages1209 - 1214
LocationIstanbul, Turkey
ISBN978-1-5090-0223-8
ISSN2374-9709
DOI10.1109/NOMS.2016.7502989
KeyTUD-CS-2016-0034
Research Areas CASED, Telecooperation, - SSI - Area Secure Smart Infrastructures, Secure Services
Abstract Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. With this in mind, the research community has been immersed in the field of IDSs over the past years more than before. Still, assessing and comparing performance between different systems and algorithms remains one of the biggest challenges in this research area. IDSs need to be evaluated and compared against high quality datasets; nevertheless, the existing ones have become outdated or lack many essential requirements. We present the Intrusion Detection Dataset Toolkit (ID2T), an approach for creating out-of-the-box labeled datasets that contain user defined attacks. In this paper, we discuss the essential requirements needed to create synthetic, yet realistic, datasets with user defined attacks. We also present typical problems found in synthetic datasets and propose a software architecture for building tools that can cope with the most typical problems. A publicly available prototype, is implemented and evaluated. The evaluation comprises a performance analysis and a quality assessment of the generated datasets. We show that our tool can handle large amounts of network traffic and that it can generate synthetic datasets without the problems or shortcomings we identified in other datasets.
Website http://www.dissect.vcu.edu/2016/
Full paper (pdf)
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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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