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SYNAPSE: Synthetic Network-Traffic Agent Pipeline for Federated Edge Security
Rajashree Shettar (R.V. College of Engineering), Reevan Coelho (R.V. College of Engineering), S S Veerasagar (R.V. College of Engineering)
Abstract: Ensuring federated edge networks’ security from intrusion attacks creates a complex problem in itself, wherein distributed nodes need to be provided with vast amounts of diverse traffic, which is stratified by protocols and needs labeling, and yet real traffic is limited due to its legal complications. Existing synthetic traffic generators address this gap inadequately. Statistical generators reproduce marginal feature distributions but discard the causal network behaviors that make attacks detectable. Static-template generators produce causally valid traffic but collapse to narrow regions of the feature space, causing federated models to overfit to fixed tool parameters rather than learning generalizable detection boundaries. Neither class of generator addresses the schema mismatches, non-independent and identically distributed partitioning requirements, or threshold calibration mismatches that arise specifically in federated deployment. This paper introduces SYNAPSE, a Synthetic Network-Traffic Agent Pipeline for federated Edge Security. SYNAPSE coordinates four tightly coupled stages: an agentic synthetic generation engine driven by a large language model operating within a Planner-Configurer-Executor-Reflector state machine over live Containernet network emulation; a deterministic schema adapter that bridges the 82-dimensional CIC-IDS2017 feature space to the 66-dimensional protocol-partitioned detector input; a federated detection mesh of four edge nodes aggregated via FedProx with trust-weighted aggregation under Dirichlet-partitioned non-independent and identically distributed data; and a privacy-preserving intelligence layer combining central differential privacy noise injected at the FedProx aggregator with an agentic retrieval-augmented generation reporting module. Across volumetric attack scenarios, SYNAPSE achieves an intrusion detection rate of 99.5 percent and a five-class detection F1 of 70.0 percent (accuracy 70.4 percent), while reducing the false positive rate by 18.3 percentage points relative to a static-template baseline. Federated convergence reaches 98.4 percent global accuracy within the target round budget, and the privacy-preserving layer retains 96.8 percent of detection F1 under a formal differential privacy guarantee.