Groundbreaking achievement: ipoque’s 5G network analysis detects unauthorized drones

This strategic partnership aims to develop cutting-edge technology capable of identifying and mitigating potential threats from unauthorized drones (UAS) by analyzing patterns within vast 5G network data streams.

Leveraging the university’s state-of-the-art 5G Campus Network, established by Deutsche Telekom Global Business in conjunction with Ericsson as part of the Digital Sensor-2-Cloud Campus Platform (DS2CCP/dtec.bw) project, the research team will focus on anomaly detection in 5G Campus Networks and drone detection in mobile networks. This innovative approach is set to bolster the security of critical infrastructure, such as airports and stadiums, against drone-based threats.

The Hamburg-based researchers, known for their development of the FALKE interceptor drone, are at the forefront of European research in this domain. ipoque will contribute its expertise in network analysis software, renowned for enhancing network optimization, management, connectivity, and security.

“This collaboration represents a crucial step towards harnessing the power of 5G network analysis for national security,” stated Prof. Gerd Scholl, leading the project at HSU/UniBw H. “By developing sophisticated anomaly detection capabilities, we can significantly enhance the protection of sensitive areas against unauthorized drone activities.”

“ipoque’s advanced software solutions, backed by nearly two decades of collected data sets and our global framework for generating high-quality training data, will be instrumental in achieving real-time network analysis and application awareness,” added Dr. Ing. Martin Mieth, Director Network Analysis for ipoque. “This synergy is pivotal for overcoming current data protection challenges and paving the way for the next generation of AI-driven network security solutions.”

Note to editors:

ipoque’s cutting-edge solution revolutionizes network analysis and security by leveraging artificial intelligence (AI) and machine learning (ML) to detect anomalies in real-time, particularly within the context of 5G Campus Networks and drone detection in mobile networks. Unlike conventional methods, ipoque’s innovative approach utilizes a vast, pre-existing pool of data sets, accumulated over nearly two decades, to train its ML models, thereby eliminating the need for customer-specific baselining and ensuring unparalleled data protection compliance. This breakthrough enables the swift and accurate identification of potential threats, such as rogue drones, and facilitates the classification of encrypted network traffic without compromising quality, making it an indispensable tool for telecommunications providers, cybersecurity entities, and government agencies seeking to fortify their networks and safeguard sensitive infrastructure.