Doctoral student position at Umeå University in the WASP NEST project AIR2: AI for Attack Identification, Response and Recovery in collaboration with leading research groups on AI and Cybersecurity from KTH and Linköping University.

Project description

This project envisions exploring time-variant learning algorithms to understand the inherent differences between benign and malicious load patterns across the cloud-edge continuum, in particular, DDoS attacks and defence strategies. The plan will explicitly model uncertainty and investigate which protocols, service functions, and dependency chains characterise benign load variations and which ones must be treated as attacks. The impact of modelled attacks will then be assessed in cloud-edge continuum scenarios, where adversaries aim, for example, resource-sharing attacks. This project will further investigate the developed defence methods with three different threat models: stealthy, dynamic, and collateral-damage caused across the continuum, and will measure the overhead of each defence strategy itself in terms of resource use and recovery time.

The PhD Student will contribute to the Autonomous Distributed Systems (ADS) Lab within the Department of Computing Science. The ADS Lab is an internationally leading research group with a focus from distributed AI to autonomous resource management and modern. The Lab currently comprises over 20 experienced and world-leading research colleagues from more than 10 different countries. For more information, see

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