Event details 19 March 2024, 3 – 4pm
Abstract Integrating machine learning (ML) models with traditional rule-based detection systems offers a promising way to improve cyberattack detection. By leveraging the scalability and pattern recognition capabilities of ML, this approach aims to improve efficiency and reduce false positives, which are critical for identifying new and sophisticated attacks. This talk provides an overview of the process, the experiments conducted, and the results of the development of an ML component for HTTP attack detection and classification.
Speaker profile Daniel Retkowitz is a Professor of Business Informatics, especially Software Engineering, at Hochschule Niederrhein University of Applied Sciences. He obtained his computer science degree from RWTH Aachen University and Chalmers University of Technology in Gothenburg, Sweden. His dissertation focused on software development for smart homes. He worked as a project manager in system development, IT architect, and senior software developer for an IT service provider in the insurance industry. Additionally, he lectured at FH Aachen University of Applied Sciences. His research and teaching focus on software engineering and machine learning.
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