6 papers
Performance Bounds for Neural Network Estimators: Applications in Fault Detection
Navid Hashemi, Mahyar Fazlyab, Justin Ruths
We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator…
Vibration transfer path analysis and path ranking for NVH optimization of a vehicle interior
Babak Sakhaei, Mohammad Durali, Navid Hashemi
By new advancements in vehicle manufacturing; evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehi…
Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks
Venkatraman Renganathan, Navid Hashemi, Justin Ruths +1
Designing resilient control strategies for mitigating stealthy attacks is a crucial task in emerging cyber-physical systems. In the design of anomaly detectors, it is common to ass…
Filtering Approaches for Dealing with Noise in Anomaly Detection
Navid Hashemi, Eduardo Verdugo German, Jonatan Pena Ramirez +1
The leading workhorse of anomaly (and attack) detection in the literature has been residual-based detectors, where the residual is the discrepancy between the observed output provi…
Generalized chi-squared detector for LTI systems with non-Gaussian noise
Navid Hashemi, Justin Ruths
Previously, we derived exact relationships between the properties of a linear time-invariant control system and properties of an anomaly detector that quantified the impact an atta…
Co-design for Security and Performance: LMI Tools
Navid Hashemi, Justin Ruths
We present a convex optimization to reduce the impact of sensor falsification attacks in linear time invariant systems controlled by observer-based feedback. We accomplish this by…