ez itt az index

SmartComLab – Network services and IoT management

Activity of the research group:

Our primary professional and scientific profile generally covers the analysis and management of communication networks and services. Lab members have considerable experience in monitoring the traffic of fixed and mobile communication networks, hardware acceleration of monitoring tasks, root cause analysis, and performance evaluation of networks and services. In addition, we are dealing with Internet of Things (IoT) platforms and their collaboration.

Recent results:

In the field of network services

• Methods for quality evaluation of Internet connections
• Methods for testing net neutrality
• Methods for hardware implementation of DPI tasks in a high-speed environment
• Methods to detect and prevent new types of DDoS attacks
• Development of network measurement systems based on the above methods

IoT management

• Efficiency testing of industrial IoT services on 4G and 5G networks
• Arrowhead framework

Special infrastructure:

GPON network infrastructure • Tektronix logic analyzer • 4G LTE mini network with 1 base station and a fully functional core

Recent projects:

H2020-Productive 4.0 • H2020-Arrowhead Tools

International relations:

Lulea University of Technology, Sweden • ISEP, Portugal • Mondragon University, Spain

Industrial partners:

IBM Zürich RL • NMHH • Aitia

Artificial Intelligence

Activity of the research group:

Our research covers the full spectrum of artificial intelligence (MI), organically linked to our group’s decades-long faculty role in general AI education, which is now present in BProf, BSc, MSc and PhD programs. Our working groups within the research group are Digital Humanities, Image Processing and Multimodal Diagnostics, Cooperative Intelligence, and Computational Biomedicine (ComBine). Digital Humanities examines semantic technologies through the processing of natural language. Image Processing and Multimodal Diagnostics explores the integration of classical image processing and information fusion methods with new artificial intelligence-based methods. Cooperative intelligence research examines federated learning, sensor networks, autonomous vehicles, multi-agent systems, and distributed computing methods. The ComBine team is developing artificial intelligence methods for bioinformatics, chemoinformatics and health informatics, with a particular focus on data and knowledge fusion, systems-based data analysis, and causal discovery.

Recent results:

We have launched an Artificial General Intelligence course to teach about the grand challenges, limits, trends, interdisciplinary nature and ethical, social dimensions of artificial intelligence. Our group is also participating in the development of a new Human-Centered Artificial Intelligence MSc program, which is part of an international EU project. We have launched and organized a student competition at the faculty in Artificial Intelligence and Machine Learning for the third time. In our research, we routinely use and constantly expand our Bayesian systems-based analysis to support biomarker discovery in several projects (UKB1602, TRAJECTOME, OTKA119866, OTKA139330). In addition to our statistical genetic research, we also focus on the development of AI methods for chemoninformatics and drug discovery, which is strengthened by our participation in national and international projects (MELLODDY). Our intelligent image processing and multimodal data fusion research has also been boosted by new projects, and we are examining the use of modern artificial intelligence tools in natural language processing in several collaborations.

Recent projects:

H2020/IMI2, MELLODDY • EUREKA, iCare4NextGen • OTKA K139330

International relations:

University of Minnesota • K.U.Leuven

Industrial partners:

SOTE • Richter • Dealogic • Abylon Kft. • E-Group • Continental

HSNLab – Future Internet

Activity of the research group:

The “Lendület” project was about research into fully automatic fault detection and repair mechanisms for the reliable operation of the Internet. We have developed network solutions that can provide more flexible and higher level services than currently available.

Recent results:

We have won several international awards such as Google Faculty Award, best paper awards at conferences. Our results propose both theoretical and practical solutions to current technical problems in telecommunications. Their strength lies in careful engineering design, efficient mathematical modelling and analysis of the problem, and finally software development to demonstrate our new solutions in operational prototype. In our analyses, we use a wide range of mathematical apparatus depending on the nature of the engineering problem, and we prefer to work with Hungarian mathematicians. Our most significant results have been in graph theory, data structures, network theory and combinatorial optimization.

Recent projects:

Lendület • TÉT • OTKA

International relations:

Technische Universität München (TUM), Germany

Industrial partners:

Ericsson

CrySyS Lab

Activity of the research group:

Our research group works in 3 domains within the field of security and privacy:

1. security of cyber-physical systems,
2. security and privacy problems in machine learning-based systems,
3. 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.