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In this paper, we propose a lightweight explainable machine learning approach that is device and attack-type agnostic and can detect IoT devices that are victims of low-intensity direct and reflective volumetric DDoS attacks launched in an ON-OFF manner. Specifically, our approach is based on a parameterized bio-inspired information-theoretic model that can capture small and subtle volumetric differences between attack versus benign byte volumes exchanged between IoT devices and the rest of the internet. Our approach has four main phases: (1) Feature Engineering involving a simple compression to achieve a universally reduced feature space for volumetric attacks; (2) Model Parameterization: identify appropriate parameters of a bio-inspired information-theoretic model and their appropriate pruned search spaces. (3) Parameter Learning: take a supervised approach for learning the optimal parameters of the explainable model using a local search. (4) Testing: We apply the learned parameters in the test set. For validation, we use real datasets from 4 different types of IoT devices containing seven different kinds of attacks and varying DDoS attack volumes. Furthermore, we employ strategies to counter the inherent biases in attacked datasets to ensure unbiased evaluation.