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Life sciences · Preprint
arXiv · September 4, 2026
Raises a question worth testing. It does not answer one.
FedIoC is a proposed federated learning framework that encodes threat indicators into gradient updates to detect coordinated cyberattack campaigns without sharing sensitive telemetry across organizations. The work is evaluated on two public benchmarks in a simulated federated setting but does not demonstrate real-world effectiveness, comparison to existing detection methods, or deployment validation.
Algorithmic framework with benchmark evaluation. Simulated federated learning clients with disjoint threat indicator sets derived from local telemetry, evaluated on two public threat-detection benchmarks.. Intervention: FedIoC: federated learning framework in which clients encode threat indicators into gradient updates via supervised contrastive loss and server clusters gradients by cosine similarity to recover campaign patterns..
Framework uses supervised contrastive loss to align gradients from clients observing the same attack campaign, enabling server-side recovery of global campaign patterns without direct indicator transmission. Evaluation on two public threat-detection benchmarks shows that federated server recovers cross-organizational campaign cohorts from gradient geometry when clients hold disjoint indicator sets. Non-IID gradient structure identified as main driver of recovery; encoder design is posed as an open problem.
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This is a methods paper presenting a novel federated learning framework for cyberattack detection, evaluated on public benchmarks without clinical outcomes, real-world deployment data, or comparison to established detection systems.
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Detecting orchestrated cyberattack campaigns that span multiple organizations traditionally requires sharing sensitive telemetry and threat intelligence across institutional boundaries and country borders, a barrier that Federated Learning removes by training shared threat detectors directly on local data. We propose FedIoC, a modular framework in which clients fold locally available structured threat indicators into their gradient updates; we instantiate the client-side encoder with a supervised contrastive loss over IoC-matched flows. Within each training batch, flows that match any known indicator pattern form the positive set; the contrastive objective pulls their learned embeddings together and pushes non-IoC embeddings away, so that campaign-relevant structure is, by design, expressed in the gradient direction. Clients sharing indicators for the same attack campaign then produce aligned gradient components, which the server clusters by the cosine similarity of their updates to recover global campaign patterns without any direct IoC transmission. We evaluate FedIoC on two public threat-detection benchmarks distributed across FL clients that each observe only a fragment of every active campaign and hold disjoint indicator sets derived from their local telemetry. In this regime the FL server recovers cross-organizational campaign cohorts directly from gradient geometry. We contribute FedIoC as a modular framework for this setting, and use it to pinpoint the non-IID gradient structure as the main driver of recovery and to define the open problem of designing encoders that improve on it.
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