Md. Emran Biswas, Md. Delowar Hossain, Mst. Tanbin Yasmin Tanny, Muhammad Aminur Rahaman, Md Al Amin, Tangina Sultana · 2026 · arXiv
DOI: 10.48550/arXiv.2610.25318 · open-access PDF
Angriffe mit Ransomware haben sich zu komplexen mehrstufigen Kampagnen entwickelt, die wichtige Infrastrukturen zielen, was zu jährlichen Schäden in Milliarden von Dollar führt. Konventionelle Erkennungstechniken sind unwirksam gegen Null-Tage-Variationen, und Datenschutzprobleme werden durch zentralisierte Machine-Learning-Algorithmen aufgeworfen. Federated Learning ermöglicht ein kooperatives Modelltraining ohne Datenfreigabe, aber aktuelle FL-Ansätze leiden unter Erläuterungsproblemen, hohen Kommunikationskosten und Nicht-IID-Datenheterogenität.
Machine translation (Meta M2M100); the English original follows.
Attacks using ransomware have developed into complex multiphase campaigns that target vital infrastructure, resulting in annual damages of billions of dollars. Conventional detection techniques are ineffective against zero-day variations, and privacy issues are raised by centralized machine learning algorithms. Federated learning allows for cooperative model training without data sharing, however current FL approaches suffer from explainability issues, high communication cost, and non-IID data heterogeneity. In this study, we present AdaFedXplain, an explainable AI-based adaptive federated lea
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