
This research aims to optimize a robust meta-learning ensemble model for malware detection on resource-constrained AIoT devices by reducing model size and inference duration while maintaining high accuracy and low false positive rates.


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optimizing-lightweight-malware-detection-models-for-resource-constrained-aiot-devices


Optimizing Lightweight Malware Detection Models for Resource-Constrained AIoT Devices


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Approximate Bayesian Computation (ABC) represents a probabilistic approach to address the challenges of comprehensive fuzz testing, which is computationally expensive and practically impossible given the infinite possible input sequences.


approximate-bayesian-computation-a-novel-approach-to-informed-fuzzing-and-vulnerability-detection


Approximate Bayesian Computation: A Novel Approach to Informed Fuzzing and Vulnerability Detection
