A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies

• URL: http://arxiv.org/abs/2511.03201v1
• Date: Wed, 05 Nov 2025 05:33:27 GMT
• Title: A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
• Authors: Hassan Wasswa, Hussein Abbass, Timothy Lynar,
• Abstract summary: Two widely used quantization strategies--Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ)--are evaluated in terms of their impact on detection performance, storage efficiency, and inference latency.<n>Results: PTQ yielded a 6x speedup and 21x reduction in size, while QAT achieved a 3x speedup and 24x compression, demonstrating the practicality of quantization for device-level IoT botnet detection.
• Score: 2.2940141855172036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an effort to counter the increasing IoT botnet-based attacks, state-of-the-art deep learning methods have been proposed and have achieved impressive detection accuracy. However, their computational intensity restricts deployment on resource-constrained IoT devices, creating a critical need for lightweight detection models. A common solution to this challenge is model compression via quantization. This study proposes a VAE-MLP model framework where an MLP-based classifier is trained on 8-dimensional latent vectors derived from the high-dimensional train data using the encoder component of a pretrained variational autoencoder (VAE). Two widely used quantization strategies--Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ)--are then systematically evaluated in terms of their impact on detection performance, storage efficiency, and inference latency using two benchmark IoT botnet datasets--N-BaIoT and CICIoT2022. The results revealed that, with respect to detection accuracy, the QAT strategy experienced a more noticeable decline,whereas PTQ incurred only a marginal reduction compared to the original unquantized model. Furthermore, PTQ yielded a 6x speedup and 21x reduction in size, while QAT achieved a 3x speedup and 24x compression, demonstrating the practicality of quantization for device-level IoT botnet detection.

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