Tackling ML-based Dynamic Mispredictions using Statically Computed Invariants for Attack Surface Reduction

Recent work has demonstrated the utility of machine learning (ML) in carrying out highly accurate predictions at runtime. One of the major challenges with using ML, however, is that the predictions lack certain guarantees. For such approaches to become practicable in security settings involving debloating and dynamic control flow monitoring, one must distinguish between mispredictions vs. attacks.