9 papers
Community Concealment from Graph Neural Networks
Dalyapraz Manatova, Pablo Moriano, L. Jean Camp
Graph neural networks (GNNs) enable powerful unsupervised learning of communities. However, such inference may inadvertently expose sensitive group structures, critical clustered p…
Evaluating False Alarm and Missing Attacks in CAN IDS
Nirab Hossain, Pablo Moriano
Modern vehicles rely on electronic control units (ECUs) interconnected through the Controller Area Network (CAN), making in-vehicle communication a critical security concern. Machi…
Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning
William Marfo, Pablo Moriano, Deepak K. Tosh +1
Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and rel…
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
Ratun Rahman, Pablo Moriano, Samee U. Khan +1
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) rec…
Community detection robustness of graph neural networks
Jaidev Goel, Pablo Moriano, Ramakrishnan Kannan +1
Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message pass…
Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN
Pablo Moriano, Steven C. Hespeler, Mingyan Li +1
Vehicular controller area networks (CANs) are susceptible to masquerade attacks by malicious adversaries. In masquerade attacks, adversaries silence a targeted ID and then send mal…