Kiril Danilchenko

Nadav Schweitzer, Kiril Danilchenko, Ariel Stulman

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 the presence of a routing-layer defense can be inferred from features derived exclusively from externally observable routing and control-plane behavior. 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 observer. 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). 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.

Kiril Danilchenko, Michael Segal, Dan Vilenchik

E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automatically, including the absence of reliable labeled data. This limitation precludes an off-the-shelf application of a machine learning pipeline. We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data. We devise a new way of sampling the labels for the training step (active learning), replacing the typical uniform sampling. Experiments on three large real-world datasets from Yelp.com show that our method outperforms state-of-the-art active learning approaches and also machine learning methods that use a much larger set of labeled data for training.