The intrusion detection at the 5G network perimeter demands learning frameworks that are practically feasible and computationally efficient. This research proposes a lightweight, slice-sensitive intrusion detection approach designed for edge deployment, with a strong emphasis on minimizing information leakage while accounting for the resource constraints inherent in edge environments. A rigorous chronological and session-discontinuous experimental protocol ensures that training and test traffic remain temporally separated, faithfully replicating realistic deployment conditions. The proposed framework employs a classical Logistic Regression classifier using flow-based statistical features ext... More >