Tianyue Chu (IMDEA Networks Institute), Alvaro Garcia-Recuero (IMDEA Networks Institute), Costas Iordanou (Cyprus University of Technology), Georgios Smaragdakis (TU Delft), Nikolaos Laoutaris (IMDEA Networks Institute)

We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing sensitive content, i.e., content related to categories such as health, political beliefs, sexual orientation, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers, it is still vulnerable to poisoning attacks from malicious users that may attempt to reduce the accuracy for benign users by disseminating faulty model updates. To guard against this, we develop a robust aggregation scheme based on subjective logic and residual-based attack detection. Employing a combination of theoretical analysis, trace-driven simulation, as well as experimental validation with a prototype and real users, we show that our classifier can detect sensitive content with high accuracy, learn new labels fast, and remain robust in view of poisoning attacks from malicious users, as well as imperfect input from non-malicious ones.

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Jung-Woo Chang (University of California San Diego), Mojan Javaheripi (University of California San Diego), Seira Hidano (KDDI Research, Inc.), Farinaz...

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Nikolas Pilavakis, Adam Jenkins, Nadin Kokciyan, Kami Vaniea (University of Edinburgh)

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Firefly: Spoofing Earth Observation Satellite Data through Radio Overshadowing

Edd Salkield, Sebastian Köhler, Simon Birnbach, Richard Baker, Martin Strohmeier, Ivan Martinovic Presenter: Edd Salkield

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Anomaly Detection in the Open World: Normality Shift Detection,...

Dongqi Han (Tsinghua University), Zhiliang Wang (Tsinghua University), Wenqi Chen (Tsinghua University), Kai Wang (Tsinghua University), Rui Yu (Tsinghua University),...

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