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We present PROVNINJA, a framework designed to generate adversarial attacks that aim to elude provenance-based Machine Learning (ML) security detectors. PROVNINJA is designed to identify and craft adversarial attack vectors that statistically mimic and impersonate system programs. Leveraging the benign execution profile of system processes commonly observed across a multitude of hosts and networks, our research proposes an efficient and effective method to probe evasive alternatives and devise stealthy attack vectors that are difficult to distinguish from benign system behaviors. PROVNINJA's suggestions for evasive attacks, originally derived in the feature space, are then translated into system actions, leading to the realization of actual evasive attack sequences in the problem space. When evaluated against State-of-The-Art (SOTA) detector models using two realistic Advanced Persistent Threat (APT) scenarios and a large collection of fileless malware samples, PROVNINJA could generate and realize evasive attack variants, reducing the detection rates by up to 59%. We also assessed PROVNINJA under varying assumptions on adversaries' knowledge and capabilities. While PROVNINJA primarily considers the black-box model, we also explored two contrasting threat models that consider blind and whitebox attack scenarios. 

This study probes the extent to which dislocations reduce carrier lifetimes and alter growth morphology and luminescence in InAs quantum dots (QD) grown on silicon. These heterostructures are key ingredients to achieving a highly reliable monolithically integrated light source on silicon necessary for photonic‐integrated circuits. Around 20%–30% shorter carrier lifetimes are found at spatially resolved individual dislocations at room temperature using time‐resolved cathodoluminescence spectroscopy, highlighting the strong nonradiative impact of dislocations even against the three‐dimensional confinement of QDs. Beyond these direct effects of increased nonradiative recombination, it is found that misfit dislocations in the defect filter layers employed during III–V/Si growth alter the QD growth environment to induce a crosshatch‐like variation in QD emission color and intensity when the filter layer is positioned sufficiently close to the QD emitter layer. Sessile threading dislocations generate even more egregious hillock defects that also reduce emission intensities by altering layer thicknesses, as measured by transmission electron microscopy and atom probe tomography. This work presents a more complete picture of the impacts of dislocations relevant to the development of light sources for scalable silicon photonic integrated circuits.