Quantization-aware Neural Architectural Search for Intrusion Detection

Authors: Rabin Yu Acharya, Laurens Le Jeune, Nele Mentens, Fatemeh Ganji, Domenic Forte
Published: 2023-11-07 | Updated: 2024-03-02

Source: https://arxiv.org/abs/2311.04194

PDF: https://arxiv.org/pdf/2311.04194

AIにより推定されたラベル

深層学習手法 量子化とプライバシー 学習の改善

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
詳細は文献データベースについてをご覧ください。

Abstract

Deploying machine learning-based intrusion detection systems (IDSs) on hardware devices is challenging due to their limited computational resources, power consumption, and network connectivity. Hence, there is a significant need for robust, deep learning models specifically designed with such constraints in mind. In this paper, we present a design methodology that automatically trains and evolves quantized neural network (NN) models that are a thousand times smaller than state-of-the-art NNs but can efficiently analyze network data for intrusion at high accuracy. In this regard, the number of LUTs utilized by this network when deployed to an FPGA is between 2.3x and 8.5x smaller with performance comparable to prior work.