Passive Reconnaissance of Routing-Layer Defenses in OLSR-Based MANETs using ML

Abstract

Mobile ad hoc networks (MANETs) based on proactive routing protocols such as OLSR, remain vulnerable to routing-layer attacks. While prior work has focused primarily on attack detection, the problem of identifying deployed defenses has received comparatively little attention. This work examines whether a routing-layer defense leaves detectable signatures in network traffic. The evaluated fictive mitigation mechanism operates entirely within standard OLSR control traffic and introduces no new packet types, making passive detection inherently difficult. Using ns-3 simulations across baseline, attack-only, defense-only, and combined attack-defense regimes under both static and mobile conditions, we derive features from observable routing dynamics and control-plane activity available to a passive attacker. Despite the restricted observability available to the adversary, the results show that defense detection remains feasible in this setting. Ensemble models achieve in-domain accuracy up to $0.91$ (AUC $0.96$). Cross-domain generalization is asymmetric: models trained on static data degrade under mobility ($\approx 0.67$), whereas mobile-trained models transfer more robustly ($\approx 0.84$). Restricting the model to a compact invariant feature subset of four metrics yields near-symmetric cross-domain transfer ($\approx 0.86$ in both directions). Further analysis shows that the cross-domain gap reflects both reduced class separability and decision-threshold transfer, with the latter largely recoverable through limited target-domain calibration. These findings indicate that the evaluated defense mechanism leaves a detectable statistical footprint in passively observable routing behavior, providing adversaries with a potential reconnaissance capability in protected MANET deployments.