Robust multi-object tracking (MOT) in distributed sensing networks is increasingly challenged by adversarial manipulation of measurements. In particular, stealthy ghost attacks generate observations that mimic genuine targets, making them difficult to distinguish using conventional trackers. While the ALARM (Average Likelihood for Attack-Resilient Multi-object) framework has recently been introduced within the Random Finite Set (RFS) paradigm, many practical systems rely on computationally efficient Kalman-based pipelines. This paper bridges this gap by proposing KF-ALARM, a distributed filtering framework that embeds the ALARM principle into the Kalman update. The method leverages cross-node track corroboration to modulate the measurement likelihood, attenuating the influence of adversarial measurements. The resulting approach achieves attack-resilient distributed MOT while preserving the efficiency and scalability of Kalman-based tracking architectures.