Presented at
DEF CON 32 (2024),
Aug. 10, 2024, 10 a.m.
(105 minutes).
As the landscape of industrial control systems (ICS) evolves, the security vulnerabilities inherent in these systems have become increasingly important. In response to this escalating situation, in this paper, we present the development of a virtualized cybersecurity research testbed tailored for these environments. Addressing the challenge of limited access to proprietary OT network data for research purposes, our this talk proposes a comprehensive framework for simulating industrial environments, aiming to facilitate the development and testing of cybersecurity solutions by providing functionalities for network traffic logging, attack impact simulation, generation of labeled multivariate time series sensor datasets, among others, bridging the gap between theoretical research and practical application needs, especially in situations of low data availability and data-driven cybersecurity research.
Presenters:
-
Borja Pintos Castro
- Researcher, Security and Privacy Area at Gradiant
Borja Pintos-Castro is passionate about cybersecurity, he spends the day reading and tinkering. He obtained a degree in Computer Engineering from the University of A Coruña. He also has a Master of Computer Security by the International University of La Rioja. Now, he is a researcher at Gradiant in the Security and Privacy Area and specifically in Cybersecurity industry 4.0 projects. Currently, he manages some industrial security projects, specifically analyzing network traffic and using honeypots to detect threats and attacks. He has the certification OSCP (PEN-200) from Offensive Security.
-
Camilo Piñón Blanco
Camilo Piñón-Blanco graduated in Telecommunication Technologies Engineering (2021) and Master in Telecommunication Engineering (2023) from the University of Vigo, both specializing in Telematics Engineering. He did his Bachelor’s Thesis with GRADIANT, focused on detection of cyber-attacks in industrial networks with Machine Learning techniques. He has worked at the atlanTTic research center as a researcher, dealing with natural language processing and text data analysis. In 2022 he re-joined the GRADIANT as an Engineer-Researcher in Security and Privacy, within the Privacy & Security Analytics line, where he has done his Master's Thesis on anomaly detection in time series through UEBA and LSTM neural networks. His main lines of work are applied machine learning, data analysis and software development.
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