 Artificial Intelligence-Driven Network Traffic and Event Classification for Future Networks THESIS TOPIC PROPOSAL Institute: Budapest University of Technology and Economics computer sciences Doctoral School of Informatics Thesis supervisor: Adrián Pekár Location of studies (in Hungarian): Department of Networked Systems and Services Abbreviation of location of studies: HIT Description of the research topic: Modern network environments produce unpredictable and fluctuating traffic patterns due to various applications and services. Ensuring the efficient and reliable operation of the network necessitates the appropriate management of these traffic flows, where artificial intelligence and machine learning can play a pivotal role. Our research focuses on real-time, machine learning-driven network traffic and event classification. Our aim is to develop advanced models and algorithms that are not only capable of swiftly and accurately classifying network traffic but can also adapt to changing environmental factors. Moreover, we place a special emphasis on optimizing network resource utilization and load distribution strategies. Throughout the project, researchers will collaborate with the latest artificial intelligence and network technologies, contributing to the shaping of the intelligent networks of the future and ensuring their adaptive and autonomous operation. Technologies such as federated learning, incremental/continual learning, deep learning, reinforcement learning, programmable data planes, network function virtualization, and edge computing will be at the forefront. This offers an excellent opportunity for those who wish to participate in an innovative and future-oriented research project that is at the cutting edge of network technologies and closely tied to the industry's current challenges. Required language skills: English Number of students who can be accepted: 2 Deadline for application: 2024-06-19 |