Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment

Lee, Jemin; Yu, Mei; Kwon, Yongin; Kim, Taeho

doi:10.1016/j.future.2022.02.005

Cited by 12 publications

(1 citation statement)

References 17 publications

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“…The deployment of an AI-based IDS framework in these devices imposes computational challenges such as restricted memory, fewer ALUs, high CPU time, etc. In such conditions, direct implementation of memory-consuming AI algorithms is unreliable without optimization (Lee et al 2022;Garifulla et al 2021). A solution to this problem is optimizing AI algorithms for IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RT-IoT2022 dataset

Sharmila,

Nagapadma

2023

Cybersecurity

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In recent years, many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks. The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices. This study proposes quantized autoencoder (QAE) model for intrusion detection systems to detect anomalies. QAE is an optimization model derived from autoencoders that incorporate pruning, clustering, and integer quantization techniques. Quantized autoencoder uint8 (QAE-u8) and quantized autoencoder float16 (QAE-f16) are two variants of QAE built to deploy computationally expensive AI models into Edge devices. First, we have generated a Real-Time Internet of Things 2022 dataset for normal and attack traffic. The autoencoder model operates on normal traffic during the training phase. The same model is then used to reconstruct anomaly traffic under the assumption that the reconstruction error (RE) of the anomaly will be high, which helps to identify the attacks. Furthermore, we study the performance of the autoencoders, QAE-u8, and QAE-f16 using accuracy, precision, recall, and F1 score through an extensive experimental study. We showed that QAE-u8 outperforms all other models with a reduction of 70.01% in average memory utilization, 92.23% in memory size compression, and 27.94% in peak CPU utilization. Thus, the proposed QAE-u8 model is more suitable for deployment on resource-constrained IoT edge devices.

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Section: Introductionmentioning

confidence: 99%