Kutatás
Budapesti Műszaki és Gazdaságtudományi Egyetem
Villamosmérnöki és Informatikai Kar
Discrete mathematics
Activity of the research group:
Fundamental research in graph theory, hypergraphs, combinatorics, combinatorial optimization, combinatorial number theory, game theory, database theory, rigidity of graphs and structures, additive combinatorics, combinatorial geometry, search theory, extremal set systems, relation between databases and code theory, graph coloring, behaviour of graph parameters in product graphs
Recent results:
Péter Pach Pál developed a new version of the polynomial method in 2016 together with Croot and Lev. This new method has led to the solution of famous problems such as the cap set problem or the Erdős-Szemerédi sunflower conjeture. Since then, the method has had many applications, such as exact bound for Green’s lemma of “arithmetical triangle removal” (Fox-Lovász), Sárközy’s theorem for polynomials over finite bodies (Green), and many others. The article was published in the most prestigious mathematical journal, Annals of Mathematics, and Fields Medal-winning mathematicians Gowers, Tao, and other leading mathematicians such as Cameron and Kalai have also analyzed it on their blogs.
Géza Tóth, together with János Pach and Gábor Tardos, proved far-reaching generalizations of the Crossing Lemma to multigraphs under various natural conditions.
Gábor Wiener, together with Peter Dameschke and Azam Sheikh Muhammad, laid the combinatorial foundations of a new, practical and well-used strict group testing model, which was published in the Journal of Combinatorial Theory A, one of the leading combinatorial journals.
Gábor Simonyi, together with Gábor Tardos, gave a partial (complete in the 4-chromatic case) characterisation of the colour-critical edges of Schrijver graphs.
Gyula Katona and László Papp, in a joint work with Ervin Győri, gave lower and upper bounds on the optimal pebbling number of large grids.
Gyula Katona, with Kitti Varga, achieved several significant results in the study of minimally tough graphs.
Recent projects:
MTA Lendület • OTKA
International relations:
Ibaraki University, Japan • Lancaster University, UK • University of Haifa, Israel • University of Warwick, UK • Ghent University • Yokohama National University • University of British Columbia, Canada • Sapienza – Universitá di Roma
Industrial partners:
Morgan Stanley • Lynx Analytics
CrySyS Lab
Activity of the research group:
Our research group works in 3 domains within the field of security and privacy:
- security of cyber-physical systems,
- security and privacy problems in machine learning-based systems,
- economics of security and privacy.
In domain (1), we work on the security of industrial automation and control systems, security of modern vehicles and intelligent transport systems, and security of IoT systems and applications. The common in these topics is that attacks originating from cyberspace may have physical consequences, resulting in equipment or environmental damage, or potentially even loss of human life, and therefore, security is an important requirement. In domain (2), we study how machine learning can be used to solve security and privacy problems, and also how machine learning–based systems may be exploited maliciously. More specifically, we focus on the security of federated learning algorithms and the problem of adversarial examples (e.g., in machine learning-based malware detection). In domain (3), we apply game theoretic models to study the incentive structures in different systems, and the cause of security and privacy problems. Besides the domains mentioned above, we have strong competency in applied cryptography, privacy enhancing technologies, malware analysis, reverse engineering, and secure operation of networks and network-based systems, including IT infrastructure automation.
Recent results:
- In the SETIT project, we developed an efficient anti-virus solution for resource constrained IoT devices, which detects malware with more than 90% accuracy.
- We developed many further security enhancing solutions for IoT devices.
- We constructed new attacks on the vehicle CAN bus, and proposed multiple attack detection mechanisms.
- We analyzed what information can be extracted from recorded CAN traffic, and showed that it is possible to identify the driver solely from the raw CAN data.
- We designed and implemented honeypots for ICS/SCADA systems.
- We achieved pioneering results in the field of interdependent privacy, which gave the theoretical explanations for the Cambridge Analytica scandal.
- We proposed new privacy-preserving federated machine learning algorithms.
Special infrastructure:
IoT devices, PLCs, industrial devices, servers • PIRAMID ICS/SCADA security testbed
Recent projects:
PrOTectME (EIT Digital) • H2020 MELLODDY • H2020 SECREDAS • H2020 SETIT
International relations:
NTNU, Trondheim, Norway • KU Leuven, Belgium • INRIA Rhones-Alpes, France • University of California, Irvine, CA • New York Institute of Technology
Industrial partners:
Microsec Zrt. • Tresorit Kft. • Ukatemi Technologies Kft.